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realtimeroboticsgroup.org / RealtimeRoboticsGroup / test / a57b70145cb0a950c1c3a9a4fddf0438c60126f2 / . / third_party / cddlib / lib-src / cddlp.c
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/* cddlp.c: dual simplex method c-code
written by Komei Fukuda, fukuda@math.ethz.ch
Version 0.94h, April 30, 2015
*/
/* cddlp.c : C-Implementation of the dual simplex method for
solving an LP: max/min A_(m-1).x subject to x in P, where
P= {x : A_i.x >= 0, i=0,...,m-2, and x_0=1}, and
A_i is the i-th row of an m x n matrix A.
Please read COPYING (GNU General Public Licence) and
the manual cddlibman.tex for detail.
*/
#include "setoper.h" /* set operation library header (Ver. May 18, 2000 or later) */
#include "cdd.h"
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <string.h>
#if defined GMPRATIONAL
#include "cdd_f.h"
#endif
#define dd_CDDLPVERSION "Version 0.94b (August 25, 2005)"
#define dd_FALSE 0
#define dd_TRUE 1
typedef set_type rowset; /* set_type defined in setoper.h */
typedef set_type colset;
void dd_CrissCrossSolve(dd_LPPtr lp,dd_ErrorType *);
void dd_DualSimplexSolve(dd_LPPtr lp,dd_ErrorType *);
void dd_CrissCrossMinimize(dd_LPPtr,dd_ErrorType *);
void dd_CrissCrossMaximize(dd_LPPtr,dd_ErrorType *);
void dd_DualSimplexMinimize(dd_LPPtr,dd_ErrorType *);
void dd_DualSimplexMaximize(dd_LPPtr,dd_ErrorType *);
void dd_FindLPBasis(dd_rowrange,dd_colrange,dd_Amatrix,dd_Bmatrix,dd_rowindex,dd_rowset,
dd_colindex,dd_rowindex,dd_rowrange,dd_colrange,
dd_colrange *,int *,dd_LPStatusType *,long *);
void dd_FindDualFeasibleBasis(dd_rowrange,dd_colrange,dd_Amatrix,dd_Bmatrix,dd_rowindex,
dd_colindex,long *,dd_rowrange,dd_colrange,dd_boolean,
dd_colrange *,dd_ErrorType *,dd_LPStatusType *,long *, long maxpivots);
#ifdef GMPRATIONAL
void dd_BasisStatus(ddf_LPPtr lpf, dd_LPPtr lp, dd_boolean*);
void dd_BasisStatusMinimize(dd_rowrange,dd_colrange, dd_Amatrix,dd_Bmatrix,dd_rowset,
dd_rowrange,dd_colrange,ddf_LPStatusType,mytype *,dd_Arow,dd_Arow,dd_rowset,ddf_colindex,
ddf_rowrange,ddf_colrange,dd_colrange *,long *, int *, int *);
void dd_BasisStatusMaximize(dd_rowrange,dd_colrange,dd_Amatrix,dd_Bmatrix,dd_rowset,
dd_rowrange,dd_colrange,ddf_LPStatusType,mytype *,dd_Arow,dd_Arow,dd_rowset,ddf_colindex,
ddf_rowrange,ddf_colrange,dd_colrange *,long *, int *, int *);
#endif
void dd_WriteBmatrix(FILE *f,dd_colrange d_size,dd_Bmatrix T);
void dd_SetNumberType(char *line,dd_NumberType *number,dd_ErrorType *Error);
void dd_ComputeRowOrderVector2(dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,
dd_rowindex OV,dd_RowOrderType ho,unsigned int rseed);
void dd_SelectPreorderedNext2(dd_rowrange m_size,dd_colrange d_size,
rowset excluded,dd_rowindex OV,dd_rowrange *hnext);
void dd_SetSolutions(dd_rowrange,dd_colrange,
dd_Amatrix,dd_Bmatrix,dd_rowrange,dd_colrange,dd_LPStatusType,
mytype *,dd_Arow,dd_Arow,dd_rowset,dd_colindex,dd_rowrange,dd_colrange,dd_rowindex);
void dd_WriteTableau(FILE *,dd_rowrange,dd_colrange,dd_Amatrix,dd_Bmatrix,
dd_colindex,dd_rowindex);
void dd_WriteSignTableau(FILE *,dd_rowrange,dd_colrange,dd_Amatrix,dd_Bmatrix,
dd_colindex,dd_rowindex);
dd_LPSolutionPtr dd_CopyLPSolution(dd_LPPtr lp)
{
dd_LPSolutionPtr lps;
dd_colrange j;
long i;
lps=(dd_LPSolutionPtr) calloc(1,sizeof(dd_LPSolutionType));
for (i=1; i<=dd_filenamelen; i++) lps->filename[i-1]=lp->filename[i-1];
lps->objective=lp->objective;
lps->solver=lp->solver;
lps->m=lp->m;
lps->d=lp->d;
lps->numbtype=lp->numbtype;
lps->LPS=lp->LPS; /* the current solution status */
dd_init(lps->optvalue);
dd_set(lps->optvalue,lp->optvalue); /* optimal value */
dd_InitializeArow(lp->d+1,&(lps->sol));
dd_InitializeArow(lp->d+1,&(lps->dsol));
lps->nbindex=(long*) calloc((lp->d)+1,sizeof(long)); /* dual solution */
for (j=0; j<=lp->d; j++){
dd_set(lps->sol[j],lp->sol[j]);
dd_set(lps->dsol[j],lp->dsol[j]);
lps->nbindex[j]=lp->nbindex[j];
}
lps->pivots[0]=lp->pivots[0];
lps->pivots[1]=lp->pivots[1];
lps->pivots[2]=lp->pivots[2];
lps->pivots[3]=lp->pivots[3];
lps->pivots[4]=lp->pivots[4];
lps->total_pivots=lp->total_pivots;
return lps;
}
dd_LPPtr dd_CreateLPData(dd_LPObjectiveType obj,
dd_NumberType nt,dd_rowrange m,dd_colrange d)
{
dd_LPType *lp;
lp=(dd_LPPtr) calloc(1,sizeof(dd_LPType));
lp->solver=dd_choiceLPSolverDefault; /* set the default lp solver */
lp->d=d;
lp->m=m;
lp->numbtype=nt;
lp->objrow=m;
lp->rhscol=1L;
lp->objective=dd_LPnone;
lp->LPS=dd_LPSundecided;
lp->eqnumber=0; /* the number of equalities */
lp->nbindex=(long*) calloc(d+1,sizeof(long));
lp->given_nbindex=(long*) calloc(d+1,sizeof(long));
set_initialize(&(lp->equalityset),m);
/* i must be in the set iff i-th row is equality . */
lp->redcheck_extensive=dd_FALSE; /* this is on only for RedundantExtensive */
lp->ired=0;
set_initialize(&(lp->redset_extra),m);
/* i is in the set if i-th row is newly recognized redundant (during the checking the row ired). */
set_initialize(&(lp->redset_accum),m);
/* i is in the set if i-th row is recognized redundant (during the checking the row ired). */
set_initialize(&(lp->posset_extra),m);
/* i is in the set if i-th row is recognized non-linearity (during the course of computation). */
lp->lexicopivot=dd_choiceLexicoPivotQ; /* dd_choice... is set in dd_set_global_constants() */
lp->m_alloc=lp->m+2;
lp->d_alloc=lp->d+2;
lp->objective=obj;
dd_InitializeBmatrix(lp->d_alloc,&(lp->B));
dd_InitializeAmatrix(lp->m_alloc,lp->d_alloc,&(lp->A));
dd_InitializeArow(lp->d_alloc,&(lp->sol));
dd_InitializeArow(lp->d_alloc,&(lp->dsol));
dd_init(lp->optvalue);
return lp;
}
dd_LPPtr dd_Matrix2LP(dd_MatrixPtr M, dd_ErrorType *err)
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPType *lp;
dd_boolean localdebug=dd_FALSE;
*err=dd_NoError;
linc=set_card(M->linset);
m=M->rowsize+1+linc;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=M->colsize;
if (localdebug) fprintf(stderr,"number of equalities = %ld\n", linc);
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_TRUE;
lp->eqnumber=linc; /* this records the number of equations */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (set_member(i, M->linset)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 1; j <= M->colsize; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-1]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
}
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-1]);
if (j==1 && i<M->rowsize && dd_Nonzero(M->matrix[i-1][j-1])) lp->Homogeneous = dd_FALSE;
} /*of j*/
} /*of i*/
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[m-1][j-1],M->rowvec[j-1]); /* objective row */
} /*of j*/
return lp;
}
dd_LPPtr dd_Matrix2Feasibility(dd_MatrixPtr M, dd_ErrorType *err)
/* Load a matrix to create an LP object for feasibility. It is
essentially the dd_Matrix2LP except that the objject function
is set to identically ZERO (maximization).
*/
/* 094 */
{
dd_rowrange m, linc;
dd_colrange j;
dd_LPType *lp;
*err=dd_NoError;
linc=set_card(M->linset);
m=M->rowsize+1+linc;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
lp=dd_Matrix2LP(M, err);
lp->objective = dd_LPmax; /* since the objective is zero, this is not important */
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[m-1][j-1],dd_purezero); /* set the objective to zero. */
} /*of j*/
return lp;
}
dd_LPPtr dd_Matrix2Feasibility2(dd_MatrixPtr M, dd_rowset R, dd_rowset S, dd_ErrorType *err)
/* Load a matrix to create an LP object for feasibility with additional equality and
strict inequality constraints given by R and S. There are three types of inequalities:
b_r + A_r x = 0 Linearity (Equations) specified by M
b_s + A_s x > 0 Strict Inequalities specified by row index set S
b_t + A_t x >= 0 The rest inequalities in M
Where the linearity is considered here as the union of linearity specified by
M and the additional set R. When S contains any linearity rows, those
rows are considered linearity (equation). Thus S does not overlide linearity.
To find a feasible solution, we set an LP
maximize z
subject to
b_r + A_r x = 0 all r in Linearity
b_s + A_s x - z >= 0 for all s in S
b_t + A_t x >= 0 for all the rest rows t
1 - z >= 0 to make the LP bounded.
Clearly, the feasibility problem has a solution iff the LP has a positive optimal value.
The variable z will be the last variable x_{d+1}.
*/
/* 094 */
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPType *lp;
dd_rowset L;
dd_boolean localdebug=dd_FALSE;
*err=dd_NoError;
set_initialize(&L, M->rowsize);
set_uni(L,M->linset,R);
linc=set_card(L);
m=M->rowsize+1+linc+1;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=M->colsize+1;
if (localdebug) fprintf(stderr,"number of equalities = %ld\n", linc);
lp=dd_CreateLPData(dd_LPmax, M->numbtype, m, d);
lp->Homogeneous = dd_TRUE;
lp->eqnumber=linc; /* this records the number of equations */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (set_member(i, L)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 1; j <= M->colsize; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-1]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
} else if (set_member(i, S)) {
dd_set(lp->A[i-1][M->colsize],dd_minusone);
}
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-1]);
if (j==1 && i<M->rowsize && dd_Nonzero(M->matrix[i-1][j-1])) lp->Homogeneous = dd_FALSE;
} /*of j*/
} /*of i*/
for (j = 1; j <= d; j++) {
dd_set(lp->A[m-2][j-1],dd_purezero); /* initialize */
} /*of j*/
dd_set(lp->A[m-2][0],dd_one); /* the bounding constraint. */
dd_set(lp->A[m-2][M->colsize],dd_minusone); /* the bounding constraint. */
for (j = 1; j <= d; j++) {
dd_set(lp->A[m-1][j-1],dd_purezero); /* initialize */
} /*of j*/
dd_set(lp->A[m-1][M->colsize],dd_one); /* maximize z */
set_free(L);
return lp;
}
void dd_FreeLPData(dd_LPPtr lp)
{
if ((lp)!=NULL){
dd_clear(lp->optvalue);
dd_FreeArow(lp->d_alloc,lp->dsol);
dd_FreeArow(lp->d_alloc,lp->sol);
dd_FreeBmatrix(lp->d_alloc,lp->B);
dd_FreeAmatrix(lp->m_alloc,lp->d_alloc,lp->A);
set_free(lp->equalityset);
set_free(lp->redset_extra);
set_free(lp->redset_accum);
set_free(lp->posset_extra);
free(lp->nbindex);
free(lp->given_nbindex);
free(lp);
}
}
void dd_FreeLPSolution(dd_LPSolutionPtr lps)
{
if (lps!=NULL){
free(lps->nbindex);
dd_FreeArow(lps->d+1,lps->dsol);
dd_FreeArow(lps->d+1,lps->sol);
dd_clear(lps->optvalue);
free(lps);
}
}
int dd_LPReverseRow(dd_LPPtr lp, dd_rowrange i)
{
dd_colrange j;
int success=0;
if (i>=1 && i<=lp->m){
lp->LPS=dd_LPSundecided;
for (j=1; j<=lp->d; j++) {
dd_neg(lp->A[i-1][j-1],lp->A[i-1][j-1]);
/* negating the i-th constraint of A */
}
success=1;
}
return success;
}
int dd_LPReplaceRow(dd_LPPtr lp, dd_rowrange i, dd_Arow a)
{
dd_colrange j;
int success=0;
if (i>=1 && i<=lp->m){
lp->LPS=dd_LPSundecided;
for (j=1; j<=lp->d; j++) {
dd_set(lp->A[i-1][j-1],a[j-1]);
/* replacing the i-th constraint by a */
}
success=1;
}
return success;
}
dd_Arow dd_LPCopyRow(dd_LPPtr lp, dd_rowrange i)
{
dd_colrange j;
dd_Arow a;
if (i>=1 && i<=lp->m){
dd_InitializeArow(lp->d, &a);
for (j=1; j<=lp->d; j++) {
dd_set(a[j-1],lp->A[i-1][j-1]);
/* copying the i-th row to a */
}
}
return a;
}
void dd_SetNumberType(char *line,dd_NumberType *number,dd_ErrorType *Error)
{
if (strncmp(line,"integer",7)==0) {
*number = dd_Integer;
return;
}
else if (strncmp(line,"rational",8)==0) {
*number = dd_Rational;
return;
}
else if (strncmp(line,"real",4)==0) {
*number = dd_Real;
return;
}
else {
*number=dd_Unknown;
*Error=dd_ImproperInputFormat;
}
}
void dd_WriteTableau(FILE *f,dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_colindex nbindex,dd_rowindex bflag)
/* Write the tableau A.T */
{
dd_colrange j;
dd_rowrange i;
mytype x;
dd_init(x);
fprintf(f," %ld %ld real\n",m_size,d_size);
fprintf(f," |");
for (j=1; j<= d_size; j++) {
fprintf(f," %ld",nbindex[j]);
} fprintf(f,"\n");
for (j=1; j<= d_size+1; j++) {
fprintf(f," ----");
} fprintf(f,"\n");
for (i=1; i<= m_size; i++) {
fprintf(f," %3ld(%3ld) |",i,bflag[i]);
for (j=1; j<= d_size; j++) {
dd_TableauEntry(&x,m_size,d_size,A,T,i,j);
dd_WriteNumber(f,x);
}
fprintf(f,"\n");
}
fprintf(f,"end\n");
dd_clear(x);
}
void dd_WriteSignTableau(FILE *f,dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_colindex nbindex,dd_rowindex bflag)
/* Write the sign tableau A.T */
{
dd_colrange j;
dd_rowrange i;
mytype x;
dd_init(x);
fprintf(f," %ld %ld real\n",m_size,d_size);
fprintf(f," |");
for (j=1; j<= d_size; j++) {
fprintf(f,"%3ld",nbindex[j]);
} fprintf(f,"\n ------- | ");
for (j=1; j<= d_size; j++) {
fprintf(f,"---");
} fprintf(f,"\n");
for (i=1; i<= m_size; i++) {
fprintf(f," %3ld(%3ld) |",i,bflag[i]);
for (j=1; j<= d_size; j++) {
dd_TableauEntry(&x,m_size,d_size,A,T,i,j);
if (dd_Positive(x)) fprintf(f, " +");
else if (dd_Negative(x)) fprintf(f, " -");
else fprintf(f, " 0");
}
fprintf(f,"\n");
}
fprintf(f,"end\n");
dd_clear(x);
}
void dd_WriteSignTableau2(FILE *f,dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_colindex nbindex_ref, dd_colindex nbindex,dd_rowindex bflag)
/* Write the sign tableau A.T and the reference basis */
{
dd_colrange j;
dd_rowrange i;
mytype x;
dd_init(x);
fprintf(f," %ld %ld real\n",m_size,d_size);
fprintf(f," |");
for (j=1; j<= d_size; j++) fprintf(f,"%3ld",nbindex_ref[j]);
fprintf(f,"\n |");
for (j=1; j<= d_size; j++) {
fprintf(f,"%3ld",nbindex[j]);
} fprintf(f,"\n ------- | ");
for (j=1; j<= d_size; j++) {
fprintf(f,"---");
} fprintf(f,"\n");
for (i=1; i<= m_size; i++) {
fprintf(f," %3ld(%3ld) |",i,bflag[i]);
for (j=1; j<= d_size; j++) {
dd_TableauEntry(&x,m_size,d_size,A,T,i,j);
if (dd_Positive(x)) fprintf(f, " +");
else if (dd_Negative(x)) fprintf(f, " -");
else fprintf(f, " 0");
}
fprintf(f,"\n");
}
fprintf(f,"end\n");
dd_clear(x);
}
void dd_GetRedundancyInformation(dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_colindex nbindex,dd_rowindex bflag, dd_rowset redset)
/* Some basic variables that are forced to be nonnegative will be output. These are
variables whose dictionary row components are all nonnegative. */
{
dd_colrange j;
dd_rowrange i;
mytype x;
dd_boolean red=dd_FALSE,localdebug=dd_FALSE;
long numbred=0;
dd_init(x);
for (i=1; i<= m_size; i++) {
red=dd_TRUE;
for (j=1; j<= d_size; j++) {
dd_TableauEntry(&x,m_size,d_size,A,T,i,j);
if (red && dd_Negative(x)) red=dd_FALSE;
}
if (bflag[i]<0 && red) {
numbred+=1;
set_addelem(redset,i);
}
}
if (localdebug) fprintf(stderr,"\ndd_GetRedundancyInformation: %ld redundant rows over %ld\n",numbred, m_size);
dd_clear(x);
}
void dd_SelectDualSimplexPivot(dd_rowrange m_size,dd_colrange d_size,
int Phase1,dd_Amatrix A,dd_Bmatrix T,dd_rowindex OV,
dd_colindex nbindex_ref, dd_colindex nbindex,dd_rowindex bflag,
dd_rowrange objrow,dd_colrange rhscol, dd_boolean lexicopivot,
dd_rowrange *r,dd_colrange *s,int *selected,dd_LPStatusType *lps)
{
/* selects a dual simplex pivot (*r,*s) if the current
basis is dual feasible and not optimal. If not dual feasible,
the procedure returns *selected=dd_FALSE and *lps=LPSundecided.
If Phase1=dd_TRUE, the RHS column will be considered as the negative
of the column of the largest variable (==m_size). For this case, it is assumed
that the caller used the auxiliary row (with variable m_size) to make the current
dictionary dual feasible before calling this routine so that the nonbasic
column for m_size corresponds to the auxiliary variable.
*/
dd_boolean colselected=dd_FALSE,rowselected=dd_FALSE,
dualfeasible=dd_TRUE,localdebug=dd_FALSE;
dd_rowrange i,iref;
dd_colrange j,k;
mytype val,valn, minval,rat,minrat;
static dd_Arow rcost;
static dd_colrange d_last=0;
static dd_colset tieset,stieset; /* store the column indices with tie */
dd_init(val); dd_init(valn); dd_init(minval); dd_init(rat); dd_init(minrat);
if (d_last<d_size) {
if (d_last>0) {
for (j=1; j<=d_last; j++){ dd_clear(rcost[j-1]);}
free(rcost);
set_free(tieset);
set_free(stieset);
}
rcost=(mytype*) calloc(d_size,sizeof(mytype));
for (j=1; j<=d_size; j++){ dd_init(rcost[j-1]);}
set_initialize(&tieset,d_size);
set_initialize(&stieset,d_size);
d_last=d_size;
}
*r=0; *s=0;
*selected=dd_FALSE;
*lps=dd_LPSundecided;
for (j=1; j<=d_size; j++){
if (j!=rhscol){
dd_TableauEntry(&(rcost[j-1]),m_size,d_size,A,T,objrow,j);
if (dd_Positive(rcost[j-1])) {
dualfeasible=dd_FALSE;
}
}
}
if (dualfeasible){
while ((*lps==dd_LPSundecided) && (!rowselected) && (!colselected)) {
for (i=1; i<=m_size; i++) {
if (i!=objrow && bflag[i]==-1) { /* i is a basic variable */
if (Phase1){
dd_TableauEntry(&val, m_size,d_size,A,T,i,bflag[m_size]);
dd_neg(val,val);
/* for dual Phase I. The RHS (dual objective) is the negative of the auxiliary variable column. */
}
else {dd_TableauEntry(&val,m_size,d_size,A,T,i,rhscol);}
if (dd_Smaller(val,minval)) {
*r=i;
dd_set(minval,val);
}
}
}
if (dd_Nonnegative(minval)) {
*lps=dd_Optimal;
}
else {
rowselected=dd_TRUE;
set_emptyset(tieset);
for (j=1; j<=d_size; j++){
dd_TableauEntry(&val,m_size,d_size,A,T,*r,j);
if (j!=rhscol && dd_Positive(val)) {
dd_div(rat,rcost[j-1],val);
dd_neg(rat,rat);
if (*s==0 || dd_Smaller(rat,minrat)){
dd_set(minrat,rat);
*s=j;
set_emptyset(tieset);
set_addelem(tieset, j);
} else if (dd_Equal(rat,minrat)){
set_addelem(tieset,j);
}
}
}
if (*s>0) {
if (!lexicopivot || set_card(tieset)==1){
colselected=dd_TRUE; *selected=dd_TRUE;
} else { /* lexicographic rule with respect to the given reference cobasis. */
if (localdebug) {printf("Tie occurred at:"); set_write(tieset); printf("\n");
dd_WriteTableau(stderr,m_size,d_size,A,T,nbindex,bflag);
}
*s=0;
k=2; /* k runs through the column indices except RHS. */
do {
iref=nbindex_ref[k]; /* iref runs though the reference basic indices */
if (iref>0) {
j=bflag[iref];
if (j>0) {
if (set_member(j,tieset) && set_card(tieset)==1) {
*s=j;
colselected=dd_TRUE;
} else {
set_delelem(tieset, j);
/* iref is cobasic, and the corresponding col is not the pivot column except it is the last one. */
}
} else {
*s=0;
for (j=1; j<=d_size; j++){
if (set_member(j,tieset)) {
dd_TableauEntry(&val,m_size,d_size,A,T,*r,j);
dd_TableauEntry(&valn,m_size,d_size,A,T,iref,j);
if (j!=rhscol && dd_Positive(val)) {
dd_div(rat,valn,val);
if (*s==0 || dd_Smaller(rat,minrat)){
dd_set(minrat,rat);
*s=j;
set_emptyset(stieset);
set_addelem(stieset, j);
} else if (dd_Equal(rat,minrat)){
set_addelem(stieset,j);
}
}
}
}
set_copy(tieset,stieset);
if (set_card(tieset)==1) colselected=dd_TRUE;
}
}
k+=1;
} while (!colselected && k<=d_size);
*selected=dd_TRUE;
}
} else *lps=dd_Inconsistent;
}
} /* end of while */
}
if (localdebug) {
if (Phase1) fprintf(stderr,"Phase 1 : select %ld,%ld\n",*r,*s);
else fprintf(stderr,"Phase 2 : select %ld,%ld\n",*r,*s);
}
dd_clear(val); dd_clear(valn); dd_clear(minval); dd_clear(rat); dd_clear(minrat);
}
void dd_TableauEntry(mytype *x,dd_rowrange m_size, dd_colrange d_size, dd_Amatrix X, dd_Bmatrix T,
dd_rowrange r, dd_colrange s)
/* Compute the (r,s) entry of X.T */
{
dd_colrange j;
mytype temp;
dd_init(temp);
dd_set(*x,dd_purezero);
for (j=0; j< d_size; j++) {
dd_mul(temp,X[r-1][j], T[j][s-1]);
dd_add(*x, *x, temp);
}
dd_clear(temp);
}
void dd_SelectPivot2(dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_RowOrderType roworder,dd_rowindex ordervec, rowset equalityset,
dd_rowrange rowmax,rowset NopivotRow,
colset NopivotCol,dd_rowrange *r,dd_colrange *s,
dd_boolean *selected)
/* Select a position (*r,*s) in the matrix A.T such that (A.T)[*r][*s] is nonzero
The choice is feasible, i.e., not on NopivotRow and NopivotCol, and
best with respect to the specified roworder
*/
{
int stop;
dd_rowrange i,rtemp;
rowset rowexcluded;
mytype Xtemp;
dd_boolean localdebug=dd_FALSE;
stop = dd_FALSE;
localdebug=dd_debug;
dd_init(Xtemp);
set_initialize(&rowexcluded,m_size);
set_copy(rowexcluded,NopivotRow);
for (i=rowmax+1;i<=m_size;i++) {
set_addelem(rowexcluded,i); /* cannot pivot on any row > rmax */
}
*selected = dd_FALSE;
do {
rtemp=0; i=1;
while (i<=m_size && rtemp==0) { /* equalityset vars have highest priorities */
if (set_member(i,equalityset) && !set_member(i,rowexcluded)){
if (localdebug) fprintf(stderr,"marked set %ld chosen as a candidate\n",i);
rtemp=i;
}
i++;
}
if (rtemp==0) dd_SelectPreorderedNext2(m_size,d_size,rowexcluded,ordervec,&rtemp);;
if (rtemp>=1) {
*r=rtemp;
*s=1;
while (*s <= d_size && !*selected) {
dd_TableauEntry(&Xtemp,m_size,d_size,A,T,*r,*s);
if (!set_member(*s,NopivotCol) && dd_Nonzero(Xtemp)) {
*selected = dd_TRUE;
stop = dd_TRUE;
} else {
(*s)++;
}
}
if (!*selected) {
set_addelem(rowexcluded,rtemp);
}
}
else {
*r = 0;
*s = 0;
stop = dd_TRUE;
}
} while (!stop);
set_free(rowexcluded); dd_clear(Xtemp);
}
void dd_GaussianColumnPivot(dd_rowrange m_size, dd_colrange d_size,
dd_Amatrix X, dd_Bmatrix T, dd_rowrange r, dd_colrange s)
/* Update the Transformation matrix T with the pivot operation on (r,s)
This procedure performs a implicit pivot operation on the matrix X by
updating the dual basis inverse T.
*/
{
dd_colrange j, j1;
mytype Xtemp0, Xtemp1, Xtemp;
static dd_Arow Rtemp;
static dd_colrange last_d=0;
dd_init(Xtemp0); dd_init(Xtemp1); dd_init(Xtemp);
if (last_d!=d_size){
if (last_d>0) {
for (j=1; j<=last_d; j++) dd_clear(Rtemp[j-1]);
free(Rtemp);
}
Rtemp=(mytype*)calloc(d_size,sizeof(mytype));
for (j=1; j<=d_size; j++) dd_init(Rtemp[j-1]);
last_d=d_size;
}
for (j=1; j<=d_size; j++) {
dd_TableauEntry(&(Rtemp[j-1]), m_size, d_size, X, T, r,j);
}
dd_set(Xtemp0,Rtemp[s-1]);
for (j = 1; j <= d_size; j++) {
if (j != s) {
dd_div(Xtemp,Rtemp[j-1],Xtemp0);
dd_set(Xtemp1,dd_purezero);
for (j1 = 1; j1 <= d_size; j1++){
dd_mul(Xtemp1,Xtemp,T[j1-1][s - 1]);
dd_sub(T[j1-1][j-1],T[j1-1][j-1],Xtemp1);
/* T[j1-1][j-1] -= T[j1-1][s - 1] * Xtemp / Xtemp0; */
}
}
}
for (j = 1; j <= d_size; j++)
dd_div(T[j-1][s - 1],T[j-1][s - 1],Xtemp0);
dd_clear(Xtemp0); dd_clear(Xtemp1); dd_clear(Xtemp);
}
void dd_GaussianColumnPivot2(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A,dd_Bmatrix T,dd_colindex nbindex,dd_rowindex bflag,dd_rowrange r,dd_colrange s)
/* Update the Transformation matrix T with the pivot operation on (r,s)
This procedure performs a implicit pivot operation on the matrix A by
updating the dual basis inverse T.
*/
{
int localdebug=dd_FALSE;
long entering;
if (dd_debug) localdebug=dd_debug;
dd_GaussianColumnPivot(m_size,d_size,A,T,r,s);
entering=nbindex[s];
bflag[r]=s; /* the nonbasic variable r corresponds to column s */
nbindex[s]=r; /* the nonbasic variable on s column is r */
if (entering>0) bflag[entering]=-1;
/* original variables have negative index and should not affect the row index */
if (localdebug) {
fprintf(stderr,"dd_GaussianColumnPivot2\n");
fprintf(stderr," pivot: (leaving, entering) = (%ld, %ld)\n", r,entering);
fprintf(stderr, " bflag[%ld] is set to %ld\n", r, s);
}
}
void dd_ResetTableau(dd_rowrange m_size,dd_colrange d_size,dd_Bmatrix T,
dd_colindex nbindex,dd_rowindex bflag,dd_rowrange objrow,dd_colrange rhscol)
{
dd_rowrange i;
dd_colrange j;
/* Initialize T and nbindex */
for (j=1; j<=d_size; j++) nbindex[j]=-j;
nbindex[rhscol]=0;
/* RHS is already in nonbasis and is considered to be associated
with the zero-th row of input. */
dd_SetToIdentity(d_size,T);
/* Set the bflag according to nbindex */
for (i=1; i<=m_size; i++) bflag[i]=-1;
/* all basic variables have index -1 */
bflag[objrow]= 0;
/* bflag of the objective variable is 0,
different from other basic variables which have -1 */
for (j=1; j<=d_size; j++) if (nbindex[j]>0) bflag[nbindex[j]]=j;
/* bflag of a nonbasic variable is its column number */
}
void dd_SelectCrissCrossPivot(dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,dd_Bmatrix T,
dd_rowindex bflag,dd_rowrange objrow,dd_colrange rhscol,
dd_rowrange *r,dd_colrange *s,
int *selected,dd_LPStatusType *lps)
{
int colselected=dd_FALSE,rowselected=dd_FALSE;
dd_rowrange i;
mytype val;
dd_init(val);
*selected=dd_FALSE;
*lps=dd_LPSundecided;
while ((*lps==dd_LPSundecided) && (!rowselected) && (!colselected)) {
for (i=1; i<=m_size; i++) {
if (i!=objrow && bflag[i]==-1) { /* i is a basic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,i,rhscol);
if (dd_Negative(val)) {
rowselected=dd_TRUE;
*r=i;
break;
}
}
else if (bflag[i] >0) { /* i is nonbasic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,objrow,bflag[i]);
if (dd_Positive(val)) {
colselected=dd_TRUE;
*s=bflag[i];
break;
}
}
}
if ((!rowselected) && (!colselected)) {
*lps=dd_Optimal;
return;
}
else if (rowselected) {
for (i=1; i<=m_size; i++) {
if (bflag[i] >0) { /* i is nonbasic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,*r,bflag[i]);
if (dd_Positive(val)) {
colselected=dd_TRUE;
*s=bflag[i];
*selected=dd_TRUE;
break;
}
}
}
}
else if (colselected) {
for (i=1; i<=m_size; i++) {
if (i!=objrow && bflag[i]==-1) { /* i is a basic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,i,*s);
if (dd_Negative(val)) {
rowselected=dd_TRUE;
*r=i;
*selected=dd_TRUE;
break;
}
}
}
}
if (!rowselected) {
*lps=dd_DualInconsistent;
}
else if (!colselected) {
*lps=dd_Inconsistent;
}
}
dd_clear(val);
}
void dd_CrissCrossSolve(dd_LPPtr lp, dd_ErrorType *err)
{
switch (lp->objective) {
case dd_LPmax:
dd_CrissCrossMaximize(lp,err);
break;
case dd_LPmin:
dd_CrissCrossMinimize(lp,err);
break;
case dd_LPnone: *err=dd_NoLPObjective; break;
}
}
void dd_DualSimplexSolve(dd_LPPtr lp, dd_ErrorType *err)
{
switch (lp->objective) {
case dd_LPmax:
dd_DualSimplexMaximize(lp,err);
break;
case dd_LPmin:
dd_DualSimplexMinimize(lp,err);
break;
case dd_LPnone: *err=dd_NoLPObjective; break;
}
}
#ifdef GMPRATIONAL
dd_LPStatusType LPSf2LPS(ddf_LPStatusType lpsf)
{
dd_LPStatusType lps=dd_LPSundecided;
switch (lpsf) {
case ddf_LPSundecided: lps=dd_LPSundecided; break;
case ddf_Optimal: lps=dd_Optimal; break;
case ddf_Inconsistent: lps=dd_Inconsistent; break;
case ddf_DualInconsistent: lps=dd_DualInconsistent; break;
case ddf_StrucInconsistent: lps=dd_StrucInconsistent; break;
case ddf_StrucDualInconsistent: lps=dd_StrucDualInconsistent; break;
case ddf_Unbounded: lps=dd_Unbounded; break;
case ddf_DualUnbounded: lps=dd_DualUnbounded; break;
}
return lps;
}
void dd_BasisStatus(ddf_LPPtr lpf, dd_LPPtr lp, dd_boolean *LPScorrect)
{
int i;
dd_colrange se, j;
dd_boolean basisfound;
switch (lp->objective) {
case dd_LPmax:
dd_BasisStatusMaximize(lp->m,lp->d,lp->A,lp->B,lp->equalityset,lp->objrow,lp->rhscol,
lpf->LPS,&(lp->optvalue),lp->sol,lp->dsol,lp->posset_extra,lpf->nbindex,lpf->re,lpf->se,&se,lp->pivots,
&basisfound, LPScorrect);
if (*LPScorrect) {
/* printf("BasisStatus Check: the current basis is verified with GMP\n"); */
lp->LPS=LPSf2LPS(lpf->LPS);
lp->re=lpf->re;
lp->se=se;
for (j=1; j<=lp->d; j++) lp->nbindex[j]=lpf->nbindex[j];
}
for (i=1; i<=5; i++) lp->pivots[i-1]+=lpf->pivots[i-1];
break;
case dd_LPmin:
dd_BasisStatusMinimize(lp->m,lp->d,lp->A,lp->B,lp->equalityset,lp->objrow,lp->rhscol,
lpf->LPS,&(lp->optvalue),lp->sol,lp->dsol,lp->posset_extra,lpf->nbindex,lpf->re,lpf->se,&se,lp->pivots,
&basisfound, LPScorrect);
if (*LPScorrect) {
/* printf("BasisStatus Check: the current basis is verified with GMP\n"); */
lp->LPS=LPSf2LPS(lpf->LPS);
lp->re=lpf->re;
lp->se=se;
for (j=1; j<=lp->d; j++) lp->nbindex[j]=lpf->nbindex[j];
}
for (i=1; i<=5; i++) lp->pivots[i-1]+=lpf->pivots[i-1];
break;
case dd_LPnone: break;
}
}
#endif
void dd_FindLPBasis(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A, dd_Bmatrix T,dd_rowindex OV,dd_rowset equalityset, dd_colindex nbindex,
dd_rowindex bflag,dd_rowrange objrow,dd_colrange rhscol,
dd_colrange *cs,int *found,dd_LPStatusType *lps,long *pivot_no)
{
/* Find a LP basis using Gaussian pivots.
If the problem has an LP basis,
the procedure returns *found=dd_TRUE,*lps=LPSundecided and an LP basis.
If the constraint matrix A (excluding the rhs and objective) is not
column independent, there are two cases. If the dependency gives a dual
inconsistency, this returns *found=dd_FALSE, *lps=dd_StrucDualInconsistent and
the evidence column *s. Otherwise, this returns *found=dd_TRUE,
*lps=LPSundecided and an LP basis of size less than d_size. Columns j
that do not belong to the basis (i.e. cannot be chosen as pivot because
they are all zero) will be indicated in nbindex vector: nbindex[j] will
be negative and set to -j.
*/
int chosen,stop;
long pivots_p0=0,rank;
colset ColSelected;
rowset RowSelected;
mytype val;
dd_rowrange r;
dd_colrange j,s;
dd_init(val);
*found=dd_FALSE; *cs=0; rank=0;
stop=dd_FALSE;
*lps=dd_LPSundecided;
set_initialize(&RowSelected,m_size);
set_initialize(&ColSelected,d_size);
set_addelem(RowSelected,objrow);
set_addelem(ColSelected,rhscol);
stop=dd_FALSE;
do { /* Find a LP basis */
dd_SelectPivot2(m_size,d_size,A,T,dd_MinIndex,OV,equalityset,
m_size,RowSelected,ColSelected,&r,&s,&chosen);
if (chosen) {
set_addelem(RowSelected,r);
set_addelem(ColSelected,s);
dd_GaussianColumnPivot2(m_size,d_size,A,T,nbindex,bflag,r,s);
pivots_p0++;
rank++;
} else {
for (j=1;j<=d_size && *lps==dd_LPSundecided; j++) {
if (j!=rhscol && nbindex[j]<0){
dd_TableauEntry(&val,m_size,d_size,A,T,objrow,j);
if (dd_Nonzero(val)){ /* dual inconsistent */
*lps=dd_StrucDualInconsistent;
*cs=j;
/* dual inconsistent because the nonzero reduced cost */
}
}
}
if (*lps==dd_LPSundecided) *found=dd_TRUE;
/* dependent columns but not dual inconsistent. */
stop=dd_TRUE;
}
/* printf("d_size=%ld, rank=%ld\n",d_size,rank); */
if (rank==d_size-1) {
stop = dd_TRUE;
*found=dd_TRUE;
}
} while (!stop);
*pivot_no=pivots_p0;
dd_statBApivots+=pivots_p0;
set_free(RowSelected);
set_free(ColSelected);
dd_clear(val);
}
void dd_FindLPBasis2(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A, dd_Bmatrix T,dd_rowindex OV,dd_rowset equalityset, dd_colindex nbindex,
dd_rowindex bflag,dd_rowrange objrow,dd_colrange rhscol,
dd_colrange *cs,int *found,long *pivot_no)
{
/* Similar to dd_FindLPBasis but it is much simpler. This tries to recompute T for
the specified basis given by nbindex. It will return *found=dd_FALSE if the specified
basis is not a basis.
*/
int chosen,stop;
long pivots_p0=0,rank;
dd_colset ColSelected,DependentCols;
dd_rowset RowSelected, NopivotRow;
mytype val;
dd_boolean localdebug=dd_FALSE;
dd_rowrange r,negcount=0;
dd_colrange j,s;
dd_init(val);
*found=dd_FALSE; *cs=0; rank=0;
set_initialize(&RowSelected,m_size);
set_initialize(&DependentCols,d_size);
set_initialize(&ColSelected,d_size);
set_initialize(&NopivotRow,m_size);
set_addelem(RowSelected,objrow);
set_addelem(ColSelected,rhscol);
set_compl(NopivotRow, NopivotRow); /* set NopivotRow to be the groundset */
for (j=2; j<=d_size; j++)
if (nbindex[j]>0)
set_delelem(NopivotRow, nbindex[j]);
else if (nbindex[j]<0){
negcount++;
set_addelem(DependentCols, -nbindex[j]);
set_addelem(ColSelected, -nbindex[j]);
}
set_uni(RowSelected, RowSelected, NopivotRow); /* RowSelected is the set of rows not allowed to poviot on */
stop=dd_FALSE;
do { /* Find a LP basis */
dd_SelectPivot2(m_size,d_size,A,T,dd_MinIndex,OV,equalityset, m_size,RowSelected,ColSelected,&r,&s,&chosen);
if (chosen) {
set_addelem(RowSelected,r);
set_addelem(ColSelected,s);
dd_GaussianColumnPivot2(m_size,d_size,A,T,nbindex,bflag,r,s);
if (localdebug && m_size <=10){
dd_WriteBmatrix(stderr,d_size,T);
dd_WriteTableau(stderr,m_size,d_size,A,T,nbindex,bflag);
}
pivots_p0++;
rank++;
} else{
*found=dd_FALSE; /* cannot pivot on any of the spacified positions. */
stop=dd_TRUE;
}
if (rank==d_size-1-negcount) {
if (negcount){
/* Now it tries to pivot on rows that are supposed to be dependent. */
set_diff(ColSelected, ColSelected, DependentCols);
dd_SelectPivot2(m_size,d_size,A,T,dd_MinIndex,OV,equalityset, m_size,RowSelected,ColSelected,&r,&s,&chosen);
if (chosen) *found=dd_FALSE; /* not supposed to be independent */
else *found=dd_TRUE;
if (localdebug){
printf("Try to check the dependent cols:");
set_write(DependentCols);
if (chosen) printf("They are not dependent. Can still pivot on (%ld, %ld)\n",r, s);
else printf("They are indeed dependent.\n");
}
} else {
*found=dd_TRUE;
}
stop = dd_TRUE;
}
} while (!stop);
for (j=1; j<=d_size; j++) if (nbindex[j]>0) bflag[nbindex[j]]=j;
*pivot_no=pivots_p0;
set_free(RowSelected);
set_free(ColSelected);
set_free(NopivotRow);
set_free(DependentCols);
dd_clear(val);
}
void dd_FindDualFeasibleBasis(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A,dd_Bmatrix T,dd_rowindex OV,
dd_colindex nbindex,dd_rowindex bflag,dd_rowrange objrow,dd_colrange rhscol, dd_boolean lexicopivot,
dd_colrange *s,dd_ErrorType *err,dd_LPStatusType *lps,long *pivot_no, long maxpivots)
{
/* Find a dual feasible basis using Phase I of Dual Simplex method.
If the problem is dual feasible,
the procedure returns *err=NoError, *lps=LPSundecided and a dual feasible
basis. If the problem is dual infeasible, this returns
*err=NoError, *lps=DualInconsistent and the evidence column *s.
Caution: matrix A must have at least one extra row: the row space A[m_size] must
have been allocated.
*/
dd_boolean phase1,dualfeasible=dd_TRUE;
dd_boolean localdebug=dd_FALSE,chosen,stop;
dd_LPStatusType LPSphase1;
long pivots_p1=0;
dd_rowrange i,r_val;
dd_colrange j,l,ms=0,s_val,local_m_size;
mytype x,val,maxcost,axvalue,maxratio;
static dd_colrange d_last=0;
static dd_Arow rcost;
static dd_colindex nbindex_ref; /* to be used to store the initial feasible basis for lexico rule */
mytype scaling,svalue; /* random scaling mytype value */
mytype minval;
if (dd_debug) localdebug=dd_debug;
dd_init(x); dd_init(val); dd_init(scaling); dd_init(svalue); dd_init(axvalue);
dd_init(maxcost); dd_set(maxcost,dd_minuszero);
dd_init(maxratio); dd_set(maxratio,dd_minuszero);
if (d_last<d_size) {
if (d_last>0) {
for (j=1; j<=d_last; j++){ dd_clear(rcost[j-1]);}
free(rcost);
free(nbindex_ref);
}
rcost=(mytype*) calloc(d_size,sizeof(mytype));
nbindex_ref=(long*) calloc(d_size+1,sizeof(long));
for (j=1; j<=d_size; j++){ dd_init(rcost[j-1]);}
d_last=d_size;
}
*err=dd_NoError; *lps=dd_LPSundecided; *s=0;
local_m_size=m_size+1; /* increase m_size by 1 */
ms=0; /* ms will be the index of column which has the largest reduced cost */
for (j=1; j<=d_size; j++){
if (j!=rhscol){
dd_TableauEntry(&(rcost[j-1]),local_m_size,d_size,A,T,objrow,j);
if (dd_Larger(rcost[j-1],maxcost)) {dd_set(maxcost,rcost[j-1]); ms = j;}
}
}
if (dd_Positive(maxcost)) dualfeasible=dd_FALSE;
if (!dualfeasible){
for (j=1; j<=d_size; j++){
dd_set(A[local_m_size-1][j-1], dd_purezero);
for (l=1; l<=d_size; l++){
if (nbindex[l]>0) {
dd_set_si(scaling,l+10);
dd_mul(svalue,A[nbindex[l]-1][j-1],scaling);
dd_sub(A[local_m_size-1][j-1],A[local_m_size-1][j-1],svalue);
/* To make the auxiliary row (0,-11,-12,...,-d-10).
It is likely to be better than (0, -1, -1, ..., -1)
to avoid a degenerate LP. Version 093c. */
}
}
}
if (localdebug){
fprintf(stderr,"\ndd_FindDualFeasibleBasis: curruent basis is not dual feasible.\n");
fprintf(stderr,"because of the column %ld assoc. with var %ld dual cost =",
ms,nbindex[ms]);
dd_WriteNumber(stderr, maxcost);
if (localdebug) {
if (m_size <=100 && d_size <=30){
printf("\ndd_FindDualFeasibleBasis: the starting dictionary.\n");
dd_WriteTableau(stdout,m_size+1,d_size,A,T,nbindex,bflag);
}
}
}
ms=0;
/* Ratio Test: ms will be now the index of column which has the largest reduced cost
over the auxiliary row entry */
for (j=1; j<=d_size; j++){
if ((j!=rhscol) && dd_Positive(rcost[j-1])){
dd_TableauEntry(&axvalue,local_m_size,d_size,A,T,local_m_size,j);
if (dd_Nonnegative(axvalue)) {
*err=dd_NumericallyInconsistent;
/* This should not happen as they are set negative above. Quit the phase I.*/
if (localdebug) fprintf(stderr,"dd_FindDualFeasibleBasis: Numerical Inconsistency detected.\n");
goto _L99;
}
dd_neg(axvalue,axvalue);
dd_div(axvalue,rcost[j-1],axvalue); /* axvalue is the negative of ratio that is to be maximized. */
if (dd_Larger(axvalue,maxratio)) {
dd_set(maxratio,axvalue);
ms = j;
}
}
}
if (ms==0) {
*err=dd_NumericallyInconsistent; /* This should not happen. Quit the phase I.*/
if (localdebug) fprintf(stderr,"dd_FindDualFeasibleBasis: Numerical Inconsistency detected.\n");
goto _L99;
}
/* Pivot on (local_m_size,ms) so that the dual basic solution becomes feasible */
dd_GaussianColumnPivot2(local_m_size,d_size,A,T,nbindex,bflag,local_m_size,ms);
pivots_p1=pivots_p1+1;
if (localdebug) {
printf("\ndd_FindDualFeasibleBasis: Pivot on %ld %ld.\n",local_m_size,ms);
}
for (j=1; j<=d_size; j++) nbindex_ref[j]=nbindex[j];
/* set the reference basis to be the current feasible basis. */
if (localdebug){
fprintf(stderr, "Store the current feasible basis:");
for (j=1; j<=d_size; j++) fprintf(stderr, " %ld", nbindex_ref[j]);
fprintf(stderr, "\n");
if (m_size <=100 && d_size <=30)
dd_WriteSignTableau2(stdout,m_size+1,d_size,A,T,nbindex_ref,nbindex,bflag);
}
phase1=dd_TRUE; stop=dd_FALSE;
do { /* Dual Simplex Phase I */
chosen=dd_FALSE; LPSphase1=dd_LPSundecided;
if (pivots_p1>maxpivots) {
*err=dd_LPCycling;
fprintf(stderr,"max number %ld of pivots performed in Phase I. Switch to the anticycling phase.\n", maxpivots);
goto _L99; /* failure due to max no. of pivots performed */
}
dd_SelectDualSimplexPivot(local_m_size,d_size,phase1,A,T,OV,nbindex_ref,nbindex,bflag,
objrow,rhscol,lexicopivot,&r_val,&s_val,&chosen,&LPSphase1);
if (!chosen) {
/* The current dictionary is terminal. There are two cases:
dd_TableauEntry(local_m_size,d_size,A,T,objrow,ms) is negative or zero.
The first case implies dual infeasible,
and the latter implies dual feasible but local_m_size is still in nonbasis.
We must pivot in the auxiliary variable local_m_size.
*/
dd_TableauEntry(&x,local_m_size,d_size,A,T,objrow,ms);
if (dd_Negative(x)){
*err=dd_NoError; *lps=dd_DualInconsistent; *s=ms;
}
if (localdebug) {
fprintf(stderr,"\ndd_FindDualFeasibleBasis: the auxiliary variable was forced to enter the basis (# pivots = %ld).\n",pivots_p1);
fprintf(stderr," -- objrow %ld, ms %ld entry: ",objrow,ms);
dd_WriteNumber(stderr, x); fprintf(stderr,"\n");
if (dd_Negative(x)){
fprintf(stderr,"->The basis is dual inconsistent. Terminate.\n");
} else {
fprintf(stderr,"->The basis is feasible. Go to phase II.\n");
}
}
dd_init(minval);
r_val=0;
for (i=1; i<=local_m_size; i++){
if (bflag[i]<0) {
/* i is basic and not the objective variable */
dd_TableauEntry(&val,local_m_size,d_size,A,T,i,ms); /* auxiliary column*/
if (dd_Smaller(val, minval)) {
r_val=i;
dd_set(minval,val);
}
}
}
dd_clear(minval);
if (r_val==0) {
*err=dd_NumericallyInconsistent; /* This should not happen. Quit the phase I.*/
if (localdebug) fprintf(stderr,"dd_FindDualFeasibleBasis: Numerical Inconsistency detected (r_val is 0).\n");
goto _L99;
}
dd_GaussianColumnPivot2(local_m_size,d_size,A,T,nbindex,bflag,r_val,ms);
pivots_p1=pivots_p1+1;
if (localdebug) {
printf("\ndd_FindDualFeasibleBasis: make the %ld-th pivot on %ld %ld to force the auxiliary variable to enter the basis.\n",pivots_p1,r_val,ms);
if (m_size <=100 && d_size <=30)
dd_WriteSignTableau2(stdout,m_size+1,d_size,A,T,nbindex_ref,nbindex,bflag);
}
stop=dd_TRUE;
} else {
dd_GaussianColumnPivot2(local_m_size,d_size,A,T,nbindex,bflag,r_val,s_val);
pivots_p1=pivots_p1+1;
if (localdebug) {
printf("\ndd_FindDualFeasibleBasis: make a %ld-th pivot on %ld %ld\n",pivots_p1,r_val,s_val);
if (m_size <=100 && d_size <=30)
dd_WriteSignTableau2(stdout,local_m_size,d_size,A,T,nbindex_ref,nbindex,bflag);
}
if (bflag[local_m_size]<0) {
stop=dd_TRUE;
if (localdebug)
fprintf(stderr,"\nDualSimplex Phase I: the auxiliary variable entered the basis (# pivots = %ld).\nGo to phase II\n",pivots_p1);
}
}
} while(!stop);
}
_L99:
*pivot_no=pivots_p1;
dd_statDS1pivots+=pivots_p1;
dd_clear(x); dd_clear(val); dd_clear(maxcost); dd_clear(maxratio);
dd_clear(scaling); dd_clear(svalue); dd_clear(axvalue);
}
void dd_DualSimplexMinimize(dd_LPPtr lp,dd_ErrorType *err)
{
dd_colrange j;
*err=dd_NoError;
for (j=1; j<=lp->d; j++)
dd_neg(lp->A[lp->objrow-1][j-1],lp->A[lp->objrow-1][j-1]);
dd_DualSimplexMaximize(lp,err);
dd_neg(lp->optvalue,lp->optvalue);
for (j=1; j<=lp->d; j++){
if (lp->LPS!=dd_Inconsistent) {
/* Inconsistent certificate stays valid for minimization, 0.94e */
dd_neg(lp->dsol[j-1],lp->dsol[j-1]);
}
dd_neg(lp->A[lp->objrow-1][j-1],lp->A[lp->objrow-1][j-1]);
}
}
void dd_DualSimplexMaximize(dd_LPPtr lp,dd_ErrorType *err)
/*
When LP is inconsistent then lp->re returns the evidence row.
When LP is dual-inconsistent then lp->se returns the evidence column.
*/
{
int stop,chosen,phase1,found;
long pivots_ds=0,pivots_p0=0,pivots_p1=0,pivots_pc=0,maxpivots,maxpivfactor=20;
dd_boolean localdebug=dd_FALSE,localdebug1=dd_FALSE;
#if !defined GMPRATIONAL
long maxccpivots,maxccpivfactor=100;
/* criss-cross should not cycle, but with floating-point arithmetics, it happens
(very rarely). Jorg Rambau reported such an LP, in August 2003. Thanks Jorg!
*/
#endif
dd_rowrange i,r;
dd_colrange j,s;
static dd_rowindex bflag;
static long mlast=0,nlast=0;
static dd_rowindex OrderVector; /* the permutation vector to store a preordered row indeces */
static dd_colindex nbindex_ref; /* to be used to store the initial feasible basis for lexico rule */
double redpercent=0,redpercent_prev=0,redgain=0;
unsigned int rseed=1;
/* *err=dd_NoError; */
if (dd_debug) localdebug=dd_debug;
set_emptyset(lp->redset_extra);
for (i=0; i<= 4; i++) lp->pivots[i]=0;
maxpivots=maxpivfactor*lp->d; /* maximum pivots to be performed before cc pivot is applied. */
#if !defined GMPRATIONAL
maxccpivots=maxccpivfactor*lp->d; /* maximum pivots to be performed with emergency cc pivots. */
#endif
if (mlast!=lp->m || nlast!=lp->d){
if (mlast>0) { /* called previously with different lp->m */
free(OrderVector);
free(bflag);
free(nbindex_ref);
}
OrderVector=(long *)calloc(lp->m+1,sizeof(*OrderVector));
bflag=(long *) calloc(lp->m+2,sizeof(*bflag)); /* one more element for an auxiliary variable */
nbindex_ref=(long*) calloc(lp->d+1,sizeof(long));
mlast=lp->m;nlast=lp->d;
}
/* Initializing control variables. */
dd_ComputeRowOrderVector2(lp->m,lp->d,lp->A,OrderVector,dd_MinIndex,rseed);
lp->re=0; lp->se=0;
dd_ResetTableau(lp->m,lp->d,lp->B,lp->nbindex,bflag,lp->objrow,lp->rhscol);
dd_FindLPBasis(lp->m,lp->d,lp->A,lp->B,OrderVector,lp->equalityset,lp->nbindex,bflag,
lp->objrow,lp->rhscol,&s,&found,&(lp->LPS),&pivots_p0);
lp->pivots[0]=pivots_p0;
if (!found){
lp->se=s;
goto _L99;
/* No LP basis is found, and thus Inconsistent.
Output the evidence column. */
}
dd_FindDualFeasibleBasis(lp->m,lp->d,lp->A,lp->B,OrderVector,lp->nbindex,bflag,
lp->objrow,lp->rhscol,lp->lexicopivot,&s, err,&(lp->LPS),&pivots_p1, maxpivots);
lp->pivots[1]=pivots_p1;
for (j=1; j<=lp->d; j++) nbindex_ref[j]=lp->nbindex[j];
/* set the reference basis to be the current feasible basis. */
if (localdebug){
fprintf(stderr, "dd_DualSimplexMaximize: Store the current feasible basis:");
for (j=1; j<=lp->d; j++) fprintf(stderr, " %ld", nbindex_ref[j]);
fprintf(stderr, "\n");
if (lp->m <=100 && lp->d <=30)
dd_WriteSignTableau2(stdout,lp->m+1,lp->d,lp->A,lp->B,nbindex_ref,lp->nbindex,bflag);
}
if (*err==dd_LPCycling || *err==dd_NumericallyInconsistent){
if (localdebug) fprintf(stderr, "Phase I failed and thus switch to the Criss-Cross method\n");
dd_CrissCrossMaximize(lp,err);
return;
}
if (lp->LPS==dd_DualInconsistent){
lp->se=s;
goto _L99;
/* No dual feasible basis is found, and thus DualInconsistent.
Output the evidence column. */
}
/* Dual Simplex Method */
stop=dd_FALSE;
do {
chosen=dd_FALSE; lp->LPS=dd_LPSundecided; phase1=dd_FALSE;
if (pivots_ds<maxpivots) {
dd_SelectDualSimplexPivot(lp->m,lp->d,phase1,lp->A,lp->B,OrderVector,nbindex_ref,lp->nbindex,bflag,
lp->objrow,lp->rhscol,lp->lexicopivot,&r,&s,&chosen,&(lp->LPS));
}
if (chosen) {
pivots_ds=pivots_ds+1;
if (lp->redcheck_extensive) {
dd_GetRedundancyInformation(lp->m,lp->d,lp->A,lp->B,lp->nbindex, bflag, lp->redset_extra);
set_uni(lp->redset_accum, lp->redset_accum,lp->redset_extra);
redpercent=100*(double)set_card(lp->redset_extra)/(double)lp->m;
redgain=redpercent-redpercent_prev;
redpercent_prev=redpercent;
if (localdebug1){
fprintf(stderr,"\ndd_DualSimplexMaximize: Phase II pivot %ld on (%ld, %ld).\n",pivots_ds,r,s);
fprintf(stderr," redundancy %f percent: redset size = %ld\n",redpercent,set_card(lp->redset_extra));
}
}
}
if (!chosen && lp->LPS==dd_LPSundecided) {
if (localdebug1){
fprintf(stderr,"Warning: an emergency CC pivot in Phase II is performed\n");
/* In principle this should not be executed because we already have dual feasibility
attained and dual simplex pivot should have been chosen. This might occur
under floating point computation, or the case of cycling.
*/
if (localdebug && lp->m <=100 && lp->d <=30){
fprintf(stderr,"\ndd_DualSimplexMaximize: The current dictionary.\n");
dd_WriteSignTableau2(stdout,lp->m,lp->d,lp->A,lp->B,nbindex_ref,lp->nbindex,bflag);
}
}
#if !defined GMPRATIONAL
if (pivots_pc>maxccpivots) {
*err=dd_LPCycling;
stop=dd_TRUE;
goto _L99;
}
#endif
dd_SelectCrissCrossPivot(lp->m,lp->d,lp->A,lp->B,bflag,
lp->objrow,lp->rhscol,&r,&s,&chosen,&(lp->LPS));
if (chosen) pivots_pc=pivots_pc+1;
}
if (chosen) {
dd_GaussianColumnPivot2(lp->m,lp->d,lp->A,lp->B,lp->nbindex,bflag,r,s);
if (localdebug && lp->m <=100 && lp->d <=30){
fprintf(stderr,"\ndd_DualSimplexMaximize: The current dictionary.\n");
dd_WriteSignTableau2(stdout,lp->m,lp->d,lp->A,lp->B,nbindex_ref,lp->nbindex,bflag);
}
} else {
switch (lp->LPS){
case dd_Inconsistent: lp->re=r;
case dd_DualInconsistent: lp->se=s;
default: break;
}
stop=dd_TRUE;
}
} while(!stop);
_L99:
lp->pivots[2]=pivots_ds;
lp->pivots[3]=pivots_pc;
dd_statDS2pivots+=pivots_ds;
dd_statACpivots+=pivots_pc;
dd_SetSolutions(lp->m,lp->d,lp->A,lp->B,lp->objrow,lp->rhscol,lp->LPS,&(lp->optvalue),lp->sol,lp->dsol,lp->posset_extra,lp->nbindex,lp->re,lp->se,bflag);
}
void dd_CrissCrossMinimize(dd_LPPtr lp,dd_ErrorType *err)
{
dd_colrange j;
*err=dd_NoError;
for (j=1; j<=lp->d; j++)
dd_neg(lp->A[lp->objrow-1][j-1],lp->A[lp->objrow-1][j-1]);
dd_CrissCrossMaximize(lp,err);
dd_neg(lp->optvalue,lp->optvalue);
for (j=1; j<=lp->d; j++){
if (lp->LPS!=dd_Inconsistent) {
/* Inconsistent certificate stays valid for minimization, 0.94e */
dd_neg(lp->dsol[j-1],lp->dsol[j-1]);
}
dd_neg(lp->A[lp->objrow-1][j-1],lp->A[lp->objrow-1][j-1]);
}
}
void dd_CrissCrossMaximize(dd_LPPtr lp,dd_ErrorType *err)
/*
When LP is inconsistent then lp->re returns the evidence row.
When LP is dual-inconsistent then lp->se returns the evidence column.
*/
{
int stop,chosen,found;
long pivots0,pivots1;
#if !defined GMPRATIONAL
long maxpivots,maxpivfactor=1000;
/* criss-cross should not cycle, but with floating-point arithmetics, it happens
(very rarely). Jorg Rambau reported such an LP, in August 2003. Thanks Jorg!
*/
#endif
dd_rowrange i,r;
dd_colrange s;
static dd_rowindex bflag;
static long mlast=0;
static dd_rowindex OrderVector; /* the permutation vector to store a preordered row indeces */
unsigned int rseed=1;
dd_colindex nbtemp;
*err=dd_NoError;
#if !defined GMPRATIONAL
maxpivots=maxpivfactor*lp->d; /* maximum pivots to be performed when floating-point arithmetics is used. */
#endif
nbtemp=(long *) calloc(lp->d+1,sizeof(long));
for (i=0; i<= 4; i++) lp->pivots[i]=0;
if (bflag==NULL || mlast!=lp->m){
if (mlast!=lp->m && mlast>0) {
free(bflag); /* called previously with different lp->m */
free(OrderVector);
}
bflag=(long *) calloc(lp->m+1,sizeof(long));
OrderVector=(long *)calloc(lp->m+1,sizeof(long));
/* initialize only for the first time or when a larger space is needed */
mlast=lp->m;
}
/* Initializing control variables. */
dd_ComputeRowOrderVector2(lp->m,lp->d,lp->A,OrderVector,dd_MinIndex,rseed);
lp->re=0; lp->se=0; pivots1=0;
dd_ResetTableau(lp->m,lp->d,lp->B,lp->nbindex,bflag,lp->objrow,lp->rhscol);
dd_FindLPBasis(lp->m,lp->d,lp->A,lp->B,OrderVector,lp->equalityset,
lp->nbindex,bflag,lp->objrow,lp->rhscol,&s,&found,&(lp->LPS),&pivots0);
lp->pivots[0]+=pivots0;
if (!found){
lp->se=s;
goto _L99;
/* No LP basis is found, and thus Inconsistent.
Output the evidence column. */
}
stop=dd_FALSE;
do { /* Criss-Cross Method */
#if !defined GMPRATIONAL
if (pivots1>maxpivots) {
*err=dd_LPCycling;
fprintf(stderr,"max number %ld of pivots performed by the criss-cross method. Most likely due to the floating-point arithmetics error.\n", maxpivots);
goto _L99; /* failure due to max no. of pivots performed */
}
#endif
dd_SelectCrissCrossPivot(lp->m,lp->d,lp->A,lp->B,bflag,
lp->objrow,lp->rhscol,&r,&s,&chosen,&(lp->LPS));
if (chosen) {
dd_GaussianColumnPivot2(lp->m,lp->d,lp->A,lp->B,lp->nbindex,bflag,r,s);
pivots1++;
} else {
switch (lp->LPS){
case dd_Inconsistent: lp->re=r;
case dd_DualInconsistent: lp->se=s;
default: break;
}
stop=dd_TRUE;
}
} while(!stop);
_L99:
lp->pivots[1]+=pivots1;
dd_statCCpivots+=pivots1;
dd_SetSolutions(lp->m,lp->d,lp->A,lp->B,
lp->objrow,lp->rhscol,lp->LPS,&(lp->optvalue),lp->sol,lp->dsol,lp->posset_extra,lp->nbindex,lp->re,lp->se,bflag);
free(nbtemp);
}
void dd_SetSolutions(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A,dd_Bmatrix T,
dd_rowrange objrow,dd_colrange rhscol,dd_LPStatusType LPS,
mytype *optvalue,dd_Arow sol,dd_Arow dsol,dd_rowset posset, dd_colindex nbindex,
dd_rowrange re,dd_colrange se,dd_rowindex bflag)
/*
Assign the solution vectors to sol,dsol,*optvalue after solving
the LP.
*/
{
dd_rowrange i;
dd_colrange j;
mytype x,sw;
int localdebug=dd_FALSE;
dd_init(x); dd_init(sw);
if (localdebug) fprintf(stderr,"SetSolutions:\n");
switch (LPS){
case dd_Optimal:
for (j=1;j<=d_size; j++) {
dd_set(sol[j-1],T[j-1][rhscol-1]);
dd_TableauEntry(&x,m_size,d_size,A,T,objrow,j);
dd_neg(dsol[j-1],x);
dd_TableauEntry(optvalue,m_size,d_size,A,T,objrow,rhscol);
if (localdebug) {fprintf(stderr,"dsol[%ld]= ",nbindex[j]); dd_WriteNumber(stderr, dsol[j-1]); }
}
for (i=1; i<=m_size; i++) {
if (bflag[i]==-1) { /* i is a basic variable */
dd_TableauEntry(&x,m_size,d_size,A,T,i,rhscol);
if (dd_Positive(x)) set_addelem(posset, i);
}
}
break;
case dd_Inconsistent:
if (localdebug) fprintf(stderr,"SetSolutions: LP is inconsistent.\n");
for (j=1;j<=d_size; j++) {
dd_set(sol[j-1],T[j-1][rhscol-1]);
dd_TableauEntry(&x,m_size,d_size,A,T,re,j);
dd_neg(dsol[j-1],x);
if (localdebug) {fprintf(stderr,"dsol[%ld]=",nbindex[j]);
dd_WriteNumber(stderr,dsol[j-1]);
fprintf(stderr,"\n");
}
}
break;
case dd_DualInconsistent:
if (localdebug) printf( "SetSolutions: LP is dual inconsistent.\n");
for (j=1;j<=d_size; j++) {
dd_set(sol[j-1],T[j-1][se-1]);
dd_TableauEntry(&x,m_size,d_size,A,T,objrow,j);
dd_neg(dsol[j-1],x);
if (localdebug) {fprintf(stderr,"dsol[%ld]=",nbindex[j]);
dd_WriteNumber(stderr,dsol[j-1]);
fprintf(stderr,"\n");
}
}
break;
case dd_StrucDualInconsistent:
dd_TableauEntry(&x,m_size,d_size,A,T,objrow,se);
if (dd_Positive(x)) dd_set(sw,dd_one);
else dd_neg(sw,dd_one);
for (j=1;j<=d_size; j++) {
dd_mul(sol[j-1],sw,T[j-1][se-1]);
dd_TableauEntry(&x,m_size,d_size,A,T,objrow,j);
dd_neg(dsol[j-1],x);
if (localdebug) {fprintf(stderr,"dsol[%ld]= ",nbindex[j]);dd_WriteNumber(stderr,dsol[j-1]);}
}
if (localdebug) fprintf(stderr,"SetSolutions: LP is dual inconsistent.\n");
break;
default:break;
}
dd_clear(x); dd_clear(sw);
}
void dd_RandomPermutation2(dd_rowindex OV,long t,unsigned int seed)
{
long k,j,ovj;
double u,xk,r,rand_max=(double) RAND_MAX;
int localdebug=dd_FALSE;
srand(seed);
for (j=t; j>1 ; j--) {
r=rand();
u=r/rand_max;
xk=(double)(j*u +1);
k=(long)xk;
if (localdebug) fprintf(stderr,"u=%g, k=%ld, r=%g, randmax= %g\n",u,k,r,rand_max);
ovj=OV[j];
OV[j]=OV[k];
OV[k]=ovj;
if (localdebug) fprintf(stderr,"row %ld is exchanged with %ld\n",j,k);
}
}
void dd_ComputeRowOrderVector2(dd_rowrange m_size,dd_colrange d_size,dd_Amatrix A,
dd_rowindex OV,dd_RowOrderType ho,unsigned int rseed)
{
long i,itemp;
OV[0]=0;
switch (ho){
case dd_MaxIndex:
for(i=1; i<=m_size; i++) OV[i]=m_size-i+1;
break;
case dd_LexMin:
for(i=1; i<=m_size; i++) OV[i]=i;
dd_QuickSort(OV,1,m_size,A,d_size);
break;
case dd_LexMax:
for(i=1; i<=m_size; i++) OV[i]=i;
dd_QuickSort(OV,1,m_size,A,d_size);
for(i=1; i<=m_size/2;i++){ /* just reverse the order */
itemp=OV[i];
OV[i]=OV[m_size-i+1];
OV[m_size-i+1]=itemp;
}
break;
case dd_RandomRow:
for(i=1; i<=m_size; i++) OV[i]=i;
if (rseed<=0) rseed=1;
dd_RandomPermutation2(OV,m_size,rseed);
break;
case dd_MinIndex:
for(i=1; i<=m_size; i++) OV[i]=i;
break;
default:
for(i=1; i<=m_size; i++) OV[i]=i;
break;
}
}
void dd_SelectPreorderedNext2(dd_rowrange m_size,dd_colrange d_size,
rowset excluded,dd_rowindex OV,dd_rowrange *hnext)
{
dd_rowrange i,k;
*hnext=0;
for (i=1; i<=m_size && *hnext==0; i++){
k=OV[i];
if (!set_member(k,excluded)) *hnext=k ;
}
}
#ifdef GMPRATIONAL
ddf_LPObjectiveType Obj2Obj(dd_LPObjectiveType obj)
{
ddf_LPObjectiveType objf=ddf_LPnone;
switch (obj) {
case dd_LPnone: objf=ddf_LPnone; break;
case dd_LPmax: objf=ddf_LPmax; break;
case dd_LPmin: objf=ddf_LPmin; break;
}
return objf;
}
ddf_LPPtr dd_LPgmp2LPf(dd_LPPtr lp)
{
dd_rowrange i;
dd_colrange j;
ddf_LPType *lpf;
double val;
dd_boolean localdebug=dd_FALSE;
if (localdebug) fprintf(stderr,"Converting a GMP-LP to a float-LP.\n");
lpf=ddf_CreateLPData(Obj2Obj(lp->objective), ddf_Real, lp->m, lp->d);
lpf->Homogeneous = lp->Homogeneous;
lpf->eqnumber=lp->eqnumber; /* this records the number of equations */
for (i = 1; i <= lp->m; i++) {
if (set_member(i, lp->equalityset)) set_addelem(lpf->equalityset,i);
/* it is equality. Its reversed row will not be in this set */
for (j = 1; j <= lp->d; j++) {
val=mpq_get_d(lp->A[i-1][j-1]);
ddf_set_d(lpf->A[i-1][j-1],val);
} /*of j*/
} /*of i*/
return lpf;
}
#endif
dd_boolean dd_LPSolve(dd_LPPtr lp,dd_LPSolverType solver,dd_ErrorType *err)
/*
The current version of dd_LPSolve that solves an LP with floating-arithmetics first
and then with the specified arithimetics if it is GMP.
When LP is inconsistent then *re returns the evidence row.
When LP is dual-inconsistent then *se returns the evidence column.
*/
{
int i;
dd_boolean found=dd_FALSE;
#ifdef GMPRATIONAL
ddf_LPPtr lpf;
ddf_ErrorType errf;
dd_boolean LPScorrect=dd_FALSE;
dd_boolean localdebug=dd_FALSE;
if (dd_debug) localdebug=dd_debug;
#endif
*err=dd_NoError;
lp->solver=solver;
time(&lp->starttime);
#ifndef GMPRATIONAL
switch (lp->solver) {
case dd_CrissCross:
dd_CrissCrossSolve(lp,err);
break;
case dd_DualSimplex:
dd_DualSimplexSolve(lp,err);
break;
}
#else
lpf=dd_LPgmp2LPf(lp);
switch (lp->solver) {
case dd_CrissCross:
ddf_CrissCrossSolve(lpf,&errf); /* First, run with double float. */
if (errf==ddf_NoError){ /* 094a: fix for a bug reported by Dima Pasechnik */
dd_BasisStatus(lpf,lp, &LPScorrect); /* Check the basis. */
} else {LPScorrect=dd_FALSE;}
if (!LPScorrect) {
if (localdebug) printf("BasisStatus: the current basis is NOT verified with GMP. Rerun with GMP.\n");
dd_CrissCrossSolve(lp,err); /* Rerun with GMP if fails. */
} else {
if (localdebug) printf("BasisStatus: the current basis is verified with GMP. The LP Solved.\n");
}
break;
case dd_DualSimplex:
ddf_DualSimplexSolve(lpf,&errf); /* First, run with double float. */
if (errf==ddf_NoError){ /* 094a: fix for a bug reported by Dima Pasechnik */
dd_BasisStatus(lpf,lp, &LPScorrect); /* Check the basis. */
} else {LPScorrect=dd_FALSE;}
if (!LPScorrect){
if (localdebug) printf("BasisStatus: the current basis is NOT verified with GMP. Rerun with GMP.\n");
dd_DualSimplexSolve(lp,err); /* Rerun with GMP if fails. */
if (localdebug){
printf("*total number pivots = %ld (ph0 = %ld, ph1 = %ld, ph2 = %ld, ph3 = %ld, ph4 = %ld)\n",
lp->total_pivots,lp->pivots[0],lp->pivots[1],lp->pivots[2],lp->pivots[3],lp->pivots[4]);
ddf_WriteLPResult(stdout, lpf, errf);
dd_WriteLP(stdout, lp);
}
} else {
if (localdebug) printf("BasisStatus: the current basis is verified with GMP. The LP Solved.\n");
}
break;
}
ddf_FreeLPData(lpf);
#endif
time(&lp->endtime);
lp->total_pivots=0;
for (i=0; i<=4; i++) lp->total_pivots+=lp->pivots[i];
if (*err==dd_NoError) found=dd_TRUE;
return found;
}
dd_boolean dd_LPSolve0(dd_LPPtr lp,dd_LPSolverType solver,dd_ErrorType *err)
/*
The original version of dd_LPSolve that solves an LP with specified arithimetics.
When LP is inconsistent then *re returns the evidence row.
When LP is dual-inconsistent then *se returns the evidence column.
*/
{
int i;
dd_boolean found=dd_FALSE;
*err=dd_NoError;
lp->solver=solver;
time(&lp->starttime);
switch (lp->solver) {
case dd_CrissCross:
dd_CrissCrossSolve(lp,err);
break;
case dd_DualSimplex:
dd_DualSimplexSolve(lp,err);
break;
}
time(&lp->endtime);
lp->total_pivots=0;
for (i=0; i<=4; i++) lp->total_pivots+=lp->pivots[i];
if (*err==dd_NoError) found=dd_TRUE;
return found;
}
dd_LPPtr dd_MakeLPforInteriorFinding(dd_LPPtr lp)
/* Delete the objective row,
add an extra column with -1's to the matrix A,
add an extra row with (bceil, 0,...,0,-1),
add an objective row with (0,...,0,1), and
rows & columns, and change m_size and d_size accordingly, to output new_A.
This sets up the LP:
maximize x_{d+1}
s.t. A x + x_{d+1} <= b
x_{d+1} <= bm * bmax,
where bm is set to 2 by default, and bmax=max{1, b[1],...,b[m_size]}.
Note that the equalitions (linearity) in the input lp will be ignored.
*/
{
dd_rowrange m;
dd_colrange d;
dd_NumberType numbtype;
dd_LPObjectiveType obj;
dd_LPType *lpnew;
dd_rowrange i;
dd_colrange j;
mytype bm,bmax,bceil;
int localdebug=dd_FALSE;
dd_init(bm); dd_init(bmax); dd_init(bceil);
dd_add(bm,dd_one,dd_one); dd_set(bmax,dd_one);
numbtype=lp->numbtype;
m=lp->m+1;
d=lp->d+1;
obj=dd_LPmax;
lpnew=dd_CreateLPData(obj, numbtype, m, d);
for (i=1; i<=lp->m; i++) {
if (dd_Larger(lp->A[i-1][lp->rhscol-1],bmax))
dd_set(bmax,lp->A[i-1][lp->rhscol-1]);
}
dd_mul(bceil,bm,bmax);
if (localdebug) {fprintf(stderr,"bceil is set to "); dd_WriteNumber(stderr, bceil);}
for (i=1; i <= lp->m; i++) {
for (j=1; j <= lp->d; j++) {
dd_set(lpnew->A[i-1][j-1],lp->A[i-1][j-1]);
}
}
for (i=1;i<=lp->m; i++){
dd_neg(lpnew->A[i-1][lp->d],dd_one); /* new column with all minus one's */
}
for (j=1;j<=lp->d;j++){
dd_set(lpnew->A[m-2][j-1],dd_purezero); /* new row (bceil, 0,...,0,-1) */
}
dd_set(lpnew->A[m-2][0],bceil); /* new row (bceil, 0,...,0,-1) */
for (j=1;j<= d-1;j++) {
dd_set(lpnew->A[m-1][j-1],dd_purezero); /* new obj row with (0,...,0,1) */
}
dd_set(lpnew->A[m-1][d-1],dd_one); /* new obj row with (0,...,0,1) */
if (localdebug) dd_WriteAmatrix(stderr, lp->A, lp->m, lp->d);
if (localdebug) dd_WriteAmatrix(stderr, lpnew->A, lpnew->m, lpnew->d);
dd_clear(bm); dd_clear(bmax); dd_clear(bceil);
return lpnew;
}
void dd_WriteLPResult(FILE *f,dd_LPPtr lp,dd_ErrorType err)
{
long j;
fprintf(f,"* cdd LP solver result\n");
if (err!=dd_NoError) {
dd_WriteErrorMessages(f,err);
goto _L99;
}
dd_WriteProgramDescription(f);
fprintf(f,"* #constraints = %ld\n",lp->m-1);
fprintf(f,"* #variables = %ld\n",lp->d-1);
switch (lp->solver) {
case dd_DualSimplex:
fprintf(f,"* Algorithm: dual simplex algorithm\n");break;
case dd_CrissCross:
fprintf(f,"* Algorithm: criss-cross method\n");break;
}
switch (lp->objective) {
case dd_LPmax:
fprintf(f,"* maximization is chosen\n");break;
case dd_LPmin:
fprintf(f,"* minimization is chosen\n");break;
case dd_LPnone:
fprintf(f,"* no objective type (max or min) is chosen\n");break;
}
if (lp->objective==dd_LPmax||lp->objective==dd_LPmin){
fprintf(f,"* Objective function is\n");
for (j=0; j<lp->d; j++){
if (j>0 && dd_Nonnegative(lp->A[lp->objrow-1][j]) ) fprintf(f," +");
if (j>0 && (j % 5) == 0) fprintf(f,"\n");
dd_WriteNumber(f,lp->A[lp->objrow-1][j]);
if (j>0) fprintf(f," X[%3ld]",j);
}
fprintf(f,"\n");
}
switch (lp->LPS){
case dd_Optimal:
fprintf(f,"* LP status: a dual pair (x,y) of optimal solutions found.\n");
fprintf(f,"begin\n");
fprintf(f," primal_solution\n");
for (j=1; j<lp->d; j++) {
fprintf(f," %3ld : ",j);
dd_WriteNumber(f,lp->sol[j]);
fprintf(f,"\n");
}
fprintf(f," dual_solution\n");
for (j=1; j<lp->d; j++){
if (lp->nbindex[j+1]>0) {
fprintf(f," %3ld : ",lp->nbindex[j+1]);
dd_WriteNumber(f,lp->dsol[j]); fprintf(f,"\n");
}
}
fprintf(f," optimal_value : "); dd_WriteNumber(f,lp->optvalue);
fprintf(f,"\nend\n");
break;
case dd_Inconsistent:
fprintf(f,"* LP status: LP is inconsistent.\n");
fprintf(f,"* The positive combination of original inequalities with\n");
fprintf(f,"* the following coefficients will prove the inconsistency.\n");
fprintf(f,"begin\n");
fprintf(f," dual_direction\n");
fprintf(f," %3ld : ",lp->re);
dd_WriteNumber(f,dd_one); fprintf(f,"\n");
for (j=1; j<lp->d; j++){
if (lp->nbindex[j+1]>0) {
fprintf(f," %3ld : ",lp->nbindex[j+1]);
dd_WriteNumber(f,lp->dsol[j]); fprintf(f,"\n");
}
}
fprintf(f,"end\n");
break;
case dd_DualInconsistent: case dd_StrucDualInconsistent:
fprintf(f,"* LP status: LP is dual inconsistent.\n");
fprintf(f,"* The linear combination of columns with\n");
fprintf(f,"* the following coefficients will prove the dual inconsistency.\n");
fprintf(f,"* (It is also an unbounded direction for the primal LP.)\n");
fprintf(f,"begin\n");
fprintf(f," primal_direction\n");
for (j=1; j<lp->d; j++) {
fprintf(f," %3ld : ",j);
dd_WriteNumber(f,lp->sol[j]);
fprintf(f,"\n");
}
fprintf(f,"end\n");
break;
default:
break;
}
fprintf(f,"* number of pivot operations = %ld (ph0 = %ld, ph1 = %ld, ph2 = %ld, ph3 = %ld, ph4 = %ld)\n",lp->total_pivots,lp->pivots[0],lp->pivots[1],lp->pivots[2],lp->pivots[3],lp->pivots[4]);
dd_WriteLPTimes(f, lp);
_L99:;
}
dd_LPPtr dd_CreateLP_H_ImplicitLinearity(dd_MatrixPtr M)
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPPtr lp;
dd_boolean localdebug=dd_FALSE;
linc=set_card(M->linset);
m=M->rowsize+1+linc+1;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=M->colsize+1;
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_TRUE;
lp->objective = dd_LPmax;
lp->eqnumber=linc; /* this records the number of equations */
lp->redcheck_extensive=dd_FALSE; /* this is default */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (set_member(i, M->linset)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 1; j <= M->colsize; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-1]);
} /*of j*/
} else {
dd_set(lp->A[i-1][d-1],dd_minusone); /* b_I + A_I x - 1 z >= 0 (z=x_d) */
}
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-1]);
if (j==1 && i<M->rowsize && dd_Nonzero(M->matrix[i-1][j-1])) lp->Homogeneous = dd_FALSE;
} /*of j*/
} /*of i*/
dd_set(lp->A[m-2][0],dd_one); dd_set(lp->A[m-2][d-1],dd_minusone);
/* make the LP bounded. */
dd_set(lp->A[m-1][d-1],dd_one);
/* objective is to maximize z. */
if (localdebug) {
fprintf(stderr,"dd_CreateLP_H_ImplicitLinearity: an new lp is\n");
dd_WriteLP(stderr,lp);
}
return lp;
}
dd_LPPtr dd_CreateLP_V_ImplicitLinearity(dd_MatrixPtr M)
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPPtr lp;
dd_boolean localdebug=dd_FALSE;
linc=set_card(M->linset);
m=M->rowsize+1+linc+1;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=(M->colsize)+2;
/* Two more columns. This is different from the H-reprentation case */
/* The below must be modified for V-representation!!! */
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_FALSE;
lp->objective = dd_LPmax;
lp->eqnumber=linc; /* this records the number of equations */
lp->redcheck_extensive=dd_FALSE; /* this is default */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
dd_set(lp->A[i-1][0],dd_purezero); /* It is almost completely degerate LP */
if (set_member(i, M->linset)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 2; j <= (M->colsize)+1; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-2]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
} else {
dd_set(lp->A[i-1][d-1],dd_minusone); /* b_I x_0 + A_I x - 1 z >= 0 (z=x_d) */
}
for (j = 2; j <= (M->colsize)+1; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-2]);
} /*of j*/
} /*of i*/
dd_set(lp->A[m-2][0],dd_one); dd_set(lp->A[m-2][d-1],dd_minusone);
/* make the LP bounded. */
dd_set(lp->A[m-1][d-1],dd_one);
/* objective is to maximize z. */
if (localdebug) {
fprintf(stderr,"dd_CreateLP_V_ImplicitLinearity: an new lp is\n");
dd_WriteLP(stderr,lp);
}
return lp;
}
dd_LPPtr dd_CreateLP_H_Redundancy(dd_MatrixPtr M, dd_rowrange itest)
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPPtr lp;
dd_boolean localdebug=dd_FALSE;
linc=set_card(M->linset);
m=M->rowsize+1+linc;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=M->colsize;
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_TRUE;
lp->objective = dd_LPmin;
lp->eqnumber=linc; /* this records the number of equations */
lp->redcheck_extensive=dd_FALSE; /* this is default */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (set_member(i, M->linset)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 1; j <= M->colsize; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-1]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
}
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-1]);
if (j==1 && i<M->rowsize && dd_Nonzero(M->matrix[i-1][j-1])) lp->Homogeneous = dd_FALSE;
} /*of j*/
} /*of i*/
for (j = 1; j <= M->colsize; j++) {
dd_set(lp->A[m-1][j-1],M->matrix[itest-1][j-1]);
/* objective is to violate the inequality in question. */
} /*of j*/
dd_add(lp->A[itest-1][0],lp->A[itest-1][0],dd_one); /* relax the original inequality by one */
return lp;
}
dd_LPPtr dd_CreateLP_V_Redundancy(dd_MatrixPtr M, dd_rowrange itest)
{
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPPtr lp;
dd_boolean localdebug=dd_FALSE;
linc=set_card(M->linset);
m=M->rowsize+1+linc;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple. */
d=(M->colsize)+1;
/* One more column. This is different from the H-reprentation case */
/* The below must be modified for V-representation!!! */
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_FALSE;
lp->objective = dd_LPmin;
lp->eqnumber=linc; /* this records the number of equations */
lp->redcheck_extensive=dd_FALSE; /* this is default */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (i==itest){
dd_set(lp->A[i-1][0],dd_one); /* this is to make the LP bounded, ie. the min >= -1 */
} else {
dd_set(lp->A[i-1][0],dd_purezero); /* It is almost completely degerate LP */
}
if (set_member(i, M->linset)) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 2; j <= (M->colsize)+1; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-2]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
}
for (j = 2; j <= (M->colsize)+1; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-2]);
} /*of j*/
} /*of i*/
for (j = 2; j <= (M->colsize)+1; j++) {
dd_set(lp->A[m-1][j-1],M->matrix[itest-1][j-2]);
/* objective is to violate the inequality in question. */
} /*of j*/
dd_set(lp->A[m-1][0],dd_purezero); /* the constant term for the objective is zero */
if (localdebug) dd_WriteLP(stdout, lp);
return lp;
}
dd_LPPtr dd_CreateLP_V_SRedundancy(dd_MatrixPtr M, dd_rowrange itest)
{
/*
V-representation (=boundary problem)
g* = maximize
1^T b_{I-itest} x_0 + 1^T A_{I-itest} (the sum of slacks)
subject to
b_itest x_0 + A_itest x = 0 (the point has to lie on the boundary)
b_{I-itest} x_0 + A_{I-itest} x >= 0 (all nonlinearity generators in one side)
1^T b_{I-itest} x_0 + 1^T A_{I-itest} x <= 1 (to make an LP bounded)
b_L x_0 + A_L x = 0. (linearity generators)
The redundant row is strongly redundant if and only if g* is zero.
*/
dd_rowrange m, i, irev, linc;
dd_colrange d, j;
dd_LPPtr lp;
dd_boolean localdebug=dd_FALSE;
linc=set_card(M->linset);
m=M->rowsize+1+linc+2;
/* We represent each equation by two inequalities.
This is not the best way but makes the code simple.
Two extra constraints are for the first equation and the bouding inequality.
*/
d=(M->colsize)+1;
/* One more column. This is different from the H-reprentation case */
/* The below must be modified for V-representation!!! */
lp=dd_CreateLPData(M->objective, M->numbtype, m, d);
lp->Homogeneous = dd_FALSE;
lp->objective = dd_LPmax;
lp->eqnumber=linc; /* this records the number of equations */
irev=M->rowsize; /* the first row of the linc reversed inequalities. */
for (i = 1; i <= M->rowsize; i++) {
if (i==itest){
dd_set(lp->A[i-1][0],dd_purezero); /* this is a half of the boundary constraint. */
} else {
dd_set(lp->A[i-1][0],dd_purezero); /* It is almost completely degerate LP */
}
if (set_member(i, M->linset) || i==itest) {
irev=irev+1;
set_addelem(lp->equalityset,i); /* it is equality. */
/* the reversed row irev is not in the equality set. */
for (j = 2; j <= (M->colsize)+1; j++) {
dd_neg(lp->A[irev-1][j-1],M->matrix[i-1][j-2]);
} /*of j*/
if (localdebug) fprintf(stderr,"equality row %ld generates the reverse row %ld.\n",i,irev);
}
for (j = 2; j <= (M->colsize)+1; j++) {
dd_set(lp->A[i-1][j-1],M->matrix[i-1][j-2]);
dd_add(lp->A[m-1][j-1],lp->A[m-1][j-1],lp->A[i-1][j-1]); /* the objective is the sum of all ineqalities */
} /*of j*/
} /*of i*/
for (j = 2; j <= (M->colsize)+1; j++) {
dd_neg(lp->A[m-2][j-1],lp->A[m-1][j-1]);
/* to make an LP bounded. */
} /*of j*/
dd_set(lp->A[m-2][0],dd_one); /* the constant term for the bounding constraint is 1 */
if (localdebug) dd_WriteLP(stdout, lp);
return lp;
}
dd_boolean dd_Redundant(dd_MatrixPtr M, dd_rowrange itest, dd_Arow certificate, dd_ErrorType *error)
/* 092 */
{
/* Checks whether the row itest is redundant for the representation.
All linearity rows are not checked and considered NONredundant.
This code works for both H- and V-representations. A certificate is
given in the case of non-redundancy, showing a solution x violating only the itest
inequality for H-representation, a hyperplane RHS and normal (x_0, x) that
separates the itest from the rest. More explicitly, the LP to be setup is
H-representation
f* = minimize
b_itest + A_itest x
subject to
b_itest + 1 + A_itest x >= 0 (relaxed inequality to make an LP bounded)
b_{I-itest} + A_{I-itest} x >= 0 (all inequalities except for itest)
b_L + A_L x = 0. (linearity)
V-representation (=separation problem)
f* = minimize
b_itest x_0 + A_itest x
subject to
b_itest x_0 + A_itest x >= -1 (to make an LP bounded)
b_{I-itest} x_0 + A_{I-itest} x >= 0 (all nonlinearity generators except for itest in one side)
b_L x_0 + A_L x = 0. (linearity generators)
Here, the input matrix is considered as (b, A), i.e. b corresponds to the first column of input
and the row indices of input is partitioned into I and L where L is the set of linearity.
In both cases, the itest data is nonredundant if and only if the optimal value f* is negative.
The certificate has dimension one more for V-representation case.
*/
dd_colrange j;
dd_LPPtr lp;
dd_LPSolutionPtr lps;
dd_ErrorType err=dd_NoError;
dd_boolean answer=dd_FALSE,localdebug=dd_FALSE;
*error=dd_NoError;
if (set_member(itest, M->linset)){
if (localdebug) printf("The %ld th row is linearity and redundancy checking is skipped.\n",itest);
goto _L99;
}
/* Create an LP data for redundancy checking */
if (M->representation==dd_Generator){
lp=dd_CreateLP_V_Redundancy(M, itest);
} else {
lp=dd_CreateLP_H_Redundancy(M, itest);
}
dd_LPSolve(lp,dd_choiceRedcheckAlgorithm,&err);
if (err!=dd_NoError){
*error=err;
goto _L999;
} else {
lps=dd_CopyLPSolution(lp);
for (j=0; j<lps->d; j++) {
dd_set(certificate[j], lps->sol[j]);
}
if (dd_Negative(lps->optvalue)){
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th row is nonredundant.\n",itest);
} else {
answer=dd_TRUE;
if (localdebug) fprintf(stderr,"==> %ld th row is redundant.\n",itest);
}
dd_FreeLPSolution(lps);
}
_L999:
dd_FreeLPData(lp);
_L99:
return answer;
}
dd_boolean dd_RedundantExtensive(dd_MatrixPtr M, dd_rowrange itest, dd_Arow certificate,
dd_rowset *redset,dd_ErrorType *error)
/* 094 */
{
/* This uses the same LP construction as dd_Reduandant. But, while it is checking
the redundancy of itest, it also tries to find some other variable that are
redundant (i.e. forced to be nonnegative). This is expensive as it used
the complete tableau information at each DualSimplex pivot. The redset must
be initialized before this function is called.
*/
dd_colrange j;
dd_LPPtr lp;
dd_LPSolutionPtr lps;
dd_ErrorType err=dd_NoError;
dd_boolean answer=dd_FALSE,localdebug=dd_FALSE;
*error=dd_NoError;
if (set_member(itest, M->linset)){
if (localdebug) printf("The %ld th row is linearity and redundancy checking is skipped.\n",itest);
goto _L99;
}
/* Create an LP data for redundancy checking */
if (M->representation==dd_Generator){
lp=dd_CreateLP_V_Redundancy(M, itest);
} else {
lp=dd_CreateLP_H_Redundancy(M, itest);
}
lp->redcheck_extensive=dd_TRUE;
dd_LPSolve0(lp,dd_DualSimplex,&err);
if (err!=dd_NoError){
*error=err;
goto _L999;
} else {
set_copy(*redset,lp->redset_extra);
set_delelem(*redset, itest);
/* itest row might be redundant in the lp but this has nothing to do with its redundancy
in the original system M. Thus we must delete it. */
if (localdebug){
fprintf(stderr, "dd_RedundantExtensive: checking for %ld, extra redset with cardinality %ld (%ld)\n",itest,set_card(*redset),set_card(lp->redset_extra));
set_fwrite(stderr, *redset); fprintf(stderr, "\n");
}
lps=dd_CopyLPSolution(lp);
for (j=0; j<lps->d; j++) {
dd_set(certificate[j], lps->sol[j]);
}
if (dd_Negative(lps->optvalue)){
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th row is nonredundant.\n",itest);
} else {
answer=dd_TRUE;
if (localdebug) fprintf(stderr,"==> %ld th row is redundant.\n",itest);
}
dd_FreeLPSolution(lps);
}
_L999:
dd_FreeLPData(lp);
_L99:
return answer;
}
dd_rowset dd_RedundantRows(dd_MatrixPtr M, dd_ErrorType *error) /* 092 */
{
dd_rowrange i,m;
dd_colrange d;
dd_rowset redset;
dd_MatrixPtr Mcopy;
dd_Arow cvec; /* certificate */
dd_boolean localdebug=dd_TRUE;
m=M->rowsize;
if (M->representation==dd_Generator){
d=(M->colsize)+1;
} else {
d=M->colsize;
}
Mcopy=dd_MatrixCopy(M);
dd_InitializeArow(d,&cvec);
set_initialize(&redset, m);
for (i=m; i>=1; i--) {
if (dd_Redundant(Mcopy, i, cvec, error)) {
if (localdebug) printf("Iteration %ld: the row %ld is redundant.\n",m-i+1,i);
set_addelem(redset, i);
dd_MatrixRowRemove(&Mcopy, i);
} else {
if (localdebug) printf("Iteration %ld: the row %ld is essential.\n",m-i+1, i);
}
if (*error!=dd_NoError) goto _L99;
}
_L99:
dd_FreeMatrix(Mcopy);
dd_FreeArow(d, cvec);
return redset;
}
dd_boolean dd_MatrixRedundancyRemove(dd_MatrixPtr *M, dd_rowset *redset,dd_rowindex *newpos, dd_ErrorType *error) /* 094 */
{
/* It returns the set of all redundant rows. This should be called after all
implicit linearity are recognized with dd_MatrixCanonicalizeLinearity.
*/
dd_rowrange i,k,m,m1;
dd_colrange d;
dd_rowset redset1;
dd_rowindex newpos1;
dd_MatrixPtr M1=NULL;
dd_Arow cvec; /* certificate */
dd_boolean success=dd_FALSE, localdebug=dd_FALSE;
m=(*M)->rowsize;
set_initialize(redset, m);
M1=dd_MatrixSortedUniqueCopy(*M,newpos);
for (i=1; i<=m; i++){
if ((*newpos)[i]<=0) set_addelem(*redset,i);
if (localdebug) printf(" %ld:%ld",i,(*newpos)[i]);
}
if (localdebug) printf("\n");
if ((*M)->representation==dd_Generator){
d=((*M)->colsize)+1;
} else {
d=(*M)->colsize;
}
m1=M1->rowsize;
if (localdebug){
fprintf(stderr,"dd_MatrixRedundancyRemove: By sorting, %ld rows have been removed. The remaining has %ld rows.\n",m-m1,m1);
/* dd_WriteMatrix(stdout,M1); */
}
dd_InitializeArow(d,&cvec);
set_initialize(&redset1, M1->rowsize);
k=1;
do {
if (dd_RedundantExtensive(M1, k, cvec, &redset1,error)) {
set_addelem(redset1, k);
dd_MatrixRowsRemove2(&M1,redset1,&newpos1);
for (i=1; i<=m; i++){
if ((*newpos)[i]>0){
if (set_member((*newpos)[i],redset1)){
set_addelem(*redset,i);
(*newpos)[i]=0; /* now the original row i is recognized redundant and removed from M1 */
} else {
(*newpos)[i]=newpos1[(*newpos)[i]]; /* update the new pos vector */
}
}
}
set_free(redset1);
set_initialize(&redset1, M1->rowsize);
if (localdebug) {
printf("dd_MatrixRedundancyRemove: the row %ld is redundant. The new matrix has %ld rows.\n", k, M1->rowsize);
/* dd_WriteMatrix(stderr, M1); */
}
free(newpos1);
} else {
if (set_card(redset1)>0) {
dd_MatrixRowsRemove2(&M1,redset1,&newpos1);
for (i=1; i<=m; i++){
if ((*newpos)[i]>0){
if (set_member((*newpos)[i],redset1)){
set_addelem(*redset,i);
(*newpos)[i]=0; /* now the original row i is recognized redundant and removed from M1 */
} else {
(*newpos)[i]=newpos1[(*newpos)[i]]; /* update the new pos vector */
}
}
}
set_free(redset1);
set_initialize(&redset1, M1->rowsize);
free(newpos1);
}
if (localdebug) {
printf("dd_MatrixRedundancyRemove: the row %ld is essential. The new matrix has %ld rows.\n", k, M1->rowsize);
/* dd_WriteMatrix(stderr, M1); */
}
k=k+1;
}
if (*error!=dd_NoError) goto _L99;
} while (k<=M1->rowsize);
if (localdebug) dd_WriteMatrix(stderr, M1);
success=dd_TRUE;
_L99:
dd_FreeMatrix(*M);
*M=M1;
dd_FreeArow(d, cvec);
set_free(redset1);
return success;
}
dd_boolean dd_SRedundant(dd_MatrixPtr M, dd_rowrange itest, dd_Arow certificate, dd_ErrorType *error)
/* 093a */
{
/* Checks whether the row itest is strongly redundant for the representation.
A row is strongly redundant in H-representation if every point in
the polyhedron satisfies it with strict inequality.
A row is strongly redundant in V-representation if this point is in
the interior of the polyhedron.
All linearity rows are not checked and considered NOT strongly redundant.
This code works for both H- and V-representations. A certificate is
given in the case of non-redundancy, showing a solution x violating only the itest
inequality for H-representation, a hyperplane RHS and normal (x_0, x) that
separates the itest from the rest. More explicitly, the LP to be setup is
H-representation
f* = minimize
b_itest + A_itest x
subject to
b_itest + 1 + A_itest x >= 0 (relaxed inequality to make an LP bounded)
b_{I-itest} + A_{I-itest} x >= 0 (all inequalities except for itest)
b_L + A_L x = 0. (linearity)
V-representation (=separation problem)
f* = minimize
b_itest x_0 + A_itest x
subject to
b_itest x_0 + A_itest x >= -1 (to make an LP bounded)
b_{I-itest} x_0 + A_{I-itest} x >= 0 (all nonlinearity generators except for itest in one side)
b_L x_0 + A_L x = 0. (linearity generators)
Here, the input matrix is considered as (b, A), i.e. b corresponds to the first column of input
and the row indices of input is partitioned into I and L where L is the set of linearity.
In H-representation, the itest data is strongly redundant if and only if the optimal value f* is positive.
In V-representation, the itest data is redundant if and only if the optimal value f* is zero (as the LP
is homogeneous and the optimal value is always non-positive). To recognize strong redundancy, one
can set up a second LP
V-representation (=boundary problem)
g* = maximize
1^T b_{I-itest} x_0 + 1^T A_{I-itest} (the sum of slacks)
subject to
b_itest x_0 + A_itest x = 0 (the point has to lie on the boundary)
b_{I-itest} x_0 + A_{I-itest} x >= 0 (all nonlinearity generators in one side)
1^T b_{I-itest} x_0 + 1^T A_{I-itest} x <= 1 (to make an LP bounded)
b_L x_0 + A_L x = 0. (linearity generators)
The redundant row is strongly redundant if and only if g* is zero.
The certificate has dimension one more for V-representation case.
*/
dd_colrange j;
dd_LPPtr lp;
dd_LPSolutionPtr lps;
dd_ErrorType err=dd_NoError;
dd_boolean answer=dd_FALSE,localdebug=dd_FALSE;
*error=dd_NoError;
if (set_member(itest, M->linset)){
if (localdebug) printf("The %ld th row is linearity and strong redundancy checking is skipped.\n",itest);
goto _L99;
}
/* Create an LP data for redundancy checking */
if (M->representation==dd_Generator){
lp=dd_CreateLP_V_Redundancy(M, itest);
} else {
lp=dd_CreateLP_H_Redundancy(M, itest);
}
dd_LPSolve(lp,dd_choiceRedcheckAlgorithm,&err);
if (err!=dd_NoError){
*error=err;
goto _L999;
} else {
lps=dd_CopyLPSolution(lp);
for (j=0; j<lps->d; j++) {
dd_set(certificate[j], lps->sol[j]);
}
if (localdebug){
printf("Optimum value:");
dd_WriteNumber(stdout, lps->optvalue);
printf("\n");
}
if (M->representation==dd_Inequality){
if (dd_Positive(lps->optvalue)){
answer=dd_TRUE;
if (localdebug) fprintf(stderr,"==> %ld th inequality is strongly redundant.\n",itest);
} else {
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th inequality is not strongly redundant.\n",itest);
}
} else {
if (dd_Negative(lps->optvalue)){
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th point is not strongly redundant.\n",itest);
} else {
/* for V-representation, we have to solve another LP */
dd_FreeLPData(lp);
dd_FreeLPSolution(lps);
lp=dd_CreateLP_V_SRedundancy(M, itest);
dd_LPSolve(lp,dd_DualSimplex,&err);
lps=dd_CopyLPSolution(lp);
if (localdebug) dd_WriteLPResult(stdout,lp,err);
if (dd_Positive(lps->optvalue)){
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th point is not strongly redundant.\n",itest);
} else {
answer=dd_TRUE;
if (localdebug) fprintf(stderr,"==> %ld th point is strongly redundant.\n",itest);
}
}
}
dd_FreeLPSolution(lps);
}
_L999:
dd_FreeLPData(lp);
_L99:
return answer;
}
dd_rowset dd_SRedundantRows(dd_MatrixPtr M, dd_ErrorType *error) /* 093a */
{
dd_rowrange i,m;
dd_colrange d;
dd_rowset redset;
dd_MatrixPtr Mcopy;
dd_Arow cvec; /* certificate */
dd_boolean localdebug=dd_FALSE;
m=M->rowsize;
if (M->representation==dd_Generator){
d=(M->colsize)+1;
} else {
d=M->colsize;
}
Mcopy=dd_MatrixCopy(M);
dd_InitializeArow(d,&cvec);
set_initialize(&redset, m);
for (i=m; i>=1; i--) {
if (dd_SRedundant(Mcopy, i, cvec, error)) {
if (localdebug) printf("dd_SRedundantRows: the row %ld is strongly redundant.\n", i);
set_addelem(redset, i);
dd_MatrixRowRemove(&Mcopy, i);
} else {
if (localdebug) printf("dd_SRedundantRows: the row %ld is not strongly redundant.\n", i);
}
if (*error!=dd_NoError) goto _L99;
}
_L99:
dd_FreeMatrix(Mcopy);
dd_FreeArow(d, cvec);
return redset;
}
dd_rowset dd_RedundantRowsViaShooting(dd_MatrixPtr M, dd_ErrorType *error) /* 092 */
{
/*
For H-representation only and not quite reliable,
especially when floating-point arithmetic is used.
Use the ordinary (slower) method dd_RedundantRows.
*/
dd_rowrange i,m, ired, irow=0;
dd_colrange j,k,d;
dd_rowset redset;
dd_rowindex rowflag;
/* ith comp is negative if the ith inequality (i-1 st row) is redundant.
zero if it is not decided.
k > 0 if it is nonredundant and assigned to the (k-1)th row of M1.
*/
dd_MatrixPtr M1;
dd_Arow shootdir, cvec=NULL;
dd_LPPtr lp0, lp;
dd_LPSolutionPtr lps;
dd_ErrorType err;
dd_LPSolverType solver=dd_DualSimplex;
dd_boolean localdebug=dd_TRUE;
m=M->rowsize;
d=M->colsize;
M1=dd_CreateMatrix(m,d);
M1->rowsize=0; /* cheat the rowsize so that smaller matrix can be stored */
set_initialize(&redset, m);
dd_InitializeArow(d, &shootdir);
dd_InitializeArow(d, &cvec);
rowflag=(long *)calloc(m+1, sizeof(long));
/* First find some (likely) nonredundant inequalities by Interior Point Find. */
lp0=dd_Matrix2LP(M, &err);
lp=dd_MakeLPforInteriorFinding(lp0);
dd_FreeLPData(lp0);
dd_LPSolve(lp, solver, &err); /* Solve the LP */
lps=dd_CopyLPSolution(lp);
if (dd_Positive(lps->optvalue)){
/* An interior point is found. Use rayshooting to find some nonredundant
inequalities. */
for (j=1; j<d; j++){
for (k=1; k<=d; k++) dd_set(shootdir[k-1], dd_purezero);
dd_set(shootdir[j], dd_one); /* j-th unit vector */
ired=dd_RayShooting(M, lps->sol, shootdir);
if (localdebug) printf("nonredundant row %3ld found by shooting.\n", ired);
if (ired>0 && rowflag[ired]<=0) {
irow++;
rowflag[ired]=irow;
for (k=1; k<=d; k++) dd_set(M1->matrix[irow-1][k-1], M->matrix[ired-1][k-1]);
}
dd_neg(shootdir[j], dd_one); /* negative of the j-th unit vector */
ired=dd_RayShooting(M, lps->sol, shootdir);
if (localdebug) printf("nonredundant row %3ld found by shooting.\n", ired);
if (ired>0 && rowflag[ired]<=0) {
irow++;
rowflag[ired]=irow;
for (k=1; k<=d; k++) dd_set(M1->matrix[irow-1][k-1], M->matrix[ired-1][k-1]);
}
}
M1->rowsize=irow;
if (localdebug) {
printf("The initial nonredundant set is:");
for (i=1; i<=m; i++) if (rowflag[i]>0) printf(" %ld", i);
printf("\n");
}
i=1;
while(i<=m){
if (rowflag[i]==0){ /* the ith inequality is not yet checked */
if (localdebug) fprintf(stderr, "Checking redundancy of %ld th inequality\n", i);
irow++; M1->rowsize=irow;
for (k=1; k<=d; k++) dd_set(M1->matrix[irow-1][k-1], M->matrix[i-1][k-1]);
if (!dd_Redundant(M1, irow, cvec, &err)){
for (k=1; k<=d; k++) dd_sub(shootdir[k-1], cvec[k-1], lps->sol[k-1]);
ired=dd_RayShooting(M, lps->sol, shootdir);
rowflag[ired]=irow;
for (k=1; k<=d; k++) dd_set(M1->matrix[irow-1][k-1], M->matrix[ired-1][k-1]);
if (localdebug) {
fprintf(stderr, "The %ld th inequality is nonredundant for the subsystem\n", i);
fprintf(stderr, "The nonredundancy of %ld th inequality is found by shooting.\n", ired);
}
} else {
if (localdebug) fprintf(stderr, "The %ld th inequality is redundant for the subsystem and thus for the whole.\n", i);
rowflag[i]=-1;
set_addelem(redset, i);
i++;
}
} else {
i++;
}
} /* endwhile */
} else {
/* No interior point is found. Apply the standard LP technique. */
if (localdebug) printf("No interior-point is found and thus the standard LP technique will be used.\n");
redset=dd_RedundantRows(M, error);
}
dd_FreeLPData(lp);
dd_FreeLPSolution(lps);
M1->rowsize=m; M1->colsize=d; /* recover the original sizes */
dd_FreeMatrix(M1);
dd_FreeArow(d, shootdir);
dd_FreeArow(d, cvec);
free(rowflag);
return redset;
}
dd_SetFamilyPtr dd_Matrix2Adjacency(dd_MatrixPtr M, dd_ErrorType *error) /* 093 */
{
/* This is to generate the (facet) graph of a polyheron (H) V-represented by M using LPs.
Since it does not use the representation conversion, it should work for a large
scale problem.
*/
dd_rowrange i,m;
dd_colrange d;
dd_rowset redset;
dd_MatrixPtr Mcopy;
dd_SetFamilyPtr F=NULL;
m=M->rowsize;
d=M->colsize;
if (m<=0 ||d<=0) {
*error=dd_EmptyRepresentation;
goto _L999;
}
Mcopy=dd_MatrixCopy(M);
F=dd_CreateSetFamily(m, m);
for (i=1; i<=m; i++) {
if (!set_member(i, M->linset)){
set_addelem(Mcopy->linset, i);
redset=dd_RedundantRows(Mcopy, error); /* redset should contain all nonadjacent ones */
set_uni(redset, redset, Mcopy->linset); /* all linearity elements should be nonadjacent */
set_compl(F->set[i-1], redset); /* set the adjacency list of vertex i */
set_delelem(Mcopy->linset, i);
set_free(redset);
if (*error!=dd_NoError) goto _L99;
}
}
_L99:
dd_FreeMatrix(Mcopy);
_L999:
return F;
}
dd_SetFamilyPtr dd_Matrix2WeakAdjacency(dd_MatrixPtr M, dd_ErrorType *error) /* 093a */
{
/* This is to generate the weak-adjacency (facet) graph of a polyheron (H) V-represented by M using LPs.
Since it does not use the representation conversion, it should work for a large
scale problem.
*/
dd_rowrange i,m;
dd_colrange d;
dd_rowset redset;
dd_MatrixPtr Mcopy;
dd_SetFamilyPtr F=NULL;
m=M->rowsize;
d=M->colsize;
if (m<=0 ||d<=0) {
*error=dd_EmptyRepresentation;
goto _L999;
}
Mcopy=dd_MatrixCopy(M);
F=dd_CreateSetFamily(m, m);
for (i=1; i<=m; i++) {
if (!set_member(i, M->linset)){
set_addelem(Mcopy->linset, i);
redset=dd_SRedundantRows(Mcopy, error); /* redset should contain all weakly nonadjacent ones */
set_uni(redset, redset, Mcopy->linset); /* all linearity elements should be nonadjacent */
set_compl(F->set[i-1], redset); /* set the adjacency list of vertex i */
set_delelem(Mcopy->linset, i);
set_free(redset);
if (*error!=dd_NoError) goto _L99;
}
}
_L99:
dd_FreeMatrix(Mcopy);
_L999:
return F;
}
dd_boolean dd_ImplicitLinearity(dd_MatrixPtr M, dd_rowrange itest, dd_Arow certificate, dd_ErrorType *error)
/* 092 */
{
/* Checks whether the row itest is implicit linearity for the representation.
All linearity rows are not checked and considered non implicit linearity (dd_FALSE).
This code works for both H- and V-representations. A certificate is
given in the case of dd_FALSE, showing a feasible solution x satisfying the itest
strict inequality for H-representation, a hyperplane RHS and normal (x_0, x) that
separates the itest from the rest. More explicitly, the LP to be setup is
the same thing as redundancy case but with maximization:
H-representation
f* = maximize
b_itest + A_itest x
subject to
b_itest + 1 + A_itest x >= 0 (relaxed inequality. This is not necessary but kept for simplicity of the code)
b_{I-itest} + A_{I-itest} x >= 0 (all inequalities except for itest)
b_L + A_L x = 0. (linearity)
V-representation (=separation problem)
f* = maximize
b_itest x_0 + A_itest x
subject to
b_itest x_0 + A_itest x >= -1 (again, this is not necessary but kept for simplicity.)
b_{I-itest} x_0 + A_{I-itest} x >= 0 (all nonlinearity generators except for itest in one side)
b_L x_0 + A_L x = 0. (linearity generators)
Here, the input matrix is considered as (b, A), i.e. b corresponds to the first column of input
and the row indices of input is partitioned into I and L where L is the set of linearity.
In both cases, the itest data is implicit linearity if and only if the optimal value f* is nonpositive.
The certificate has dimension one more for V-representation case.
*/
dd_colrange j;
dd_LPPtr lp;
dd_LPSolutionPtr lps;
dd_ErrorType err=dd_NoError;
dd_boolean answer=dd_FALSE,localdebug=dd_FALSE;
*error=dd_NoError;
if (set_member(itest, M->linset)){
if (localdebug) printf("The %ld th row is linearity and redundancy checking is skipped.\n",itest);
goto _L99;
}
/* Create an LP data for redundancy checking */
if (M->representation==dd_Generator){
lp=dd_CreateLP_V_Redundancy(M, itest);
} else {
lp=dd_CreateLP_H_Redundancy(M, itest);
}
lp->objective = dd_LPmax; /* the lp->objective is set by CreateLP* to LPmin */
dd_LPSolve(lp,dd_choiceRedcheckAlgorithm,&err);
if (err!=dd_NoError){
*error=err;
goto _L999;
} else {
lps=dd_CopyLPSolution(lp);
for (j=0; j<lps->d; j++) {
dd_set(certificate[j], lps->sol[j]);
}
if (lps->LPS==dd_Optimal && dd_EqualToZero(lps->optvalue)){
answer=dd_TRUE;
if (localdebug) fprintf(stderr,"==> %ld th data is an implicit linearity.\n",itest);
} else {
answer=dd_FALSE;
if (localdebug) fprintf(stderr,"==> %ld th data is not an implicit linearity.\n",itest);
}
dd_FreeLPSolution(lps);
}
_L999:
dd_FreeLPData(lp);
_L99:
return answer;
}
int dd_FreeOfImplicitLinearity(dd_MatrixPtr M, dd_Arow certificate, dd_rowset *imp_linrows, dd_ErrorType *error)
/* 092 */
{
/* Checks whether the matrix M constains any implicit linearity at all.
It returns 1 if it is free of any implicit linearity. This means that
the present linearity rows define the linearity correctly. It returns
nonpositive values otherwise.
H-representation
f* = maximize z
subject to
b_I + A_I x - 1 z >= 0
b_L + A_L x = 0 (linearity)
z <= 1.
V-representation (=separation problem)
f* = maximize z
subject to
b_I x_0 + A_I x - 1 z >= 0 (all nonlinearity generators in one side)
b_L x_0 + A_L x = 0 (linearity generators)
z <= 1.
Here, the input matrix is considered as (b, A), i.e. b corresponds to the first column of input
and the row indices of input is partitioned into I and L where L is the set of linearity.
In both cases, any implicit linearity exists if and only if the optimal value f* is nonpositive.
The certificate has dimension one more for V-representation case.
*/
dd_LPPtr lp;
dd_rowrange i,m;
dd_colrange j,d1;
dd_ErrorType err=dd_NoError;
dd_Arow cvec; /* certificate for implicit linearity */
int answer=0,localdebug=dd_FALSE;
*error=dd_NoError;
/* Create an LP data for redundancy checking */
if (M->representation==dd_Generator){
lp=dd_CreateLP_V_ImplicitLinearity(M);
} else {
lp=dd_CreateLP_H_ImplicitLinearity(M);
}
dd_LPSolve(lp,dd_choiceRedcheckAlgorithm,&err);
if (err!=dd_NoError){
*error=err;
goto _L999;
} else {
for (j=0; j<lp->d; j++) {
dd_set(certificate[j], lp->sol[j]);
}
if (localdebug) dd_WriteLPResult(stderr,lp,err);
/* *posset contains a set of row indices that are recognized as nonlinearity. */
if (localdebug) {
fprintf(stderr,"==> The following variables are not implicit linearity:\n");
set_fwrite(stderr, lp->posset_extra);
fprintf(stderr,"\n");
}
if (M->representation==dd_Generator){
d1=(M->colsize)+1;
} else {
d1=M->colsize;
}
m=M->rowsize;
dd_InitializeArow(d1,&cvec);
set_initialize(imp_linrows,m);
if (lp->LPS==dd_Optimal){
if (dd_Positive(lp->optvalue)){
answer=1;
if (localdebug) fprintf(stderr,"==> The matrix has no implicit linearity.\n");
} else if (dd_Negative(lp->optvalue)) {
answer=-1;
if (localdebug) fprintf(stderr,"==> The matrix defines the trivial system.\n");
} else {
answer=0;
if (localdebug) fprintf(stderr,"==> The matrix has some implicit linearity.\n");
}
} else {
answer=-2;
if (localdebug) fprintf(stderr,"==> The LP fails.\n");
}
if (answer==0){
/* List the implicit linearity rows */
for (i=m; i>=1; i--) {
if (!set_member(i,lp->posset_extra)) {
if (dd_ImplicitLinearity(M, i, cvec, error)) {
set_addelem(*imp_linrows, i);
if (localdebug) {
fprintf(stderr," row %ld is implicit linearity\n",i);
fprintf(stderr,"\n");
}
}
if (*error!=dd_NoError) goto _L999;
}
}
} /* end of if (answer==0) */
if (answer==-1) {
for (i=m; i>=1; i--) set_addelem(*imp_linrows, i);
} /* all rows are considered implicit linearity */
dd_FreeArow(d1,cvec);
}
_L999:
dd_FreeLPData(lp);
return answer;
}
dd_rowset dd_ImplicitLinearityRows(dd_MatrixPtr M, dd_ErrorType *error) /* 092 */
{
dd_colrange d;
dd_rowset imp_linset;
dd_Arow cvec; /* certificate */
int foi;
dd_boolean localdebug=dd_FALSE;
if (M->representation==dd_Generator){
d=(M->colsize)+2;
} else {
d=M->colsize+1;
}
dd_InitializeArow(d,&cvec);
if (localdebug) fprintf(stdout, "\ndd_ImplicitLinearityRows: Check whether the system contains any implicit linearity.\n");
foi=dd_FreeOfImplicitLinearity(M, cvec, &imp_linset, error);
if (localdebug){
switch (foi) {
case 1:
fprintf(stdout, " It is free of implicit linearity.\n");
break;
case 0:
fprintf(stdout, " It is not free of implicit linearity.\n");
break;
case -1:
fprintf(stdout, " The input system is trivial (i.e. the empty H-polytope or the V-rep of the whole space.\n");
break;
default:
fprintf(stdout, " The LP was not solved correctly.\n");
break;
}
}
if (localdebug){
fprintf(stderr, " Implicit linearity rows are:\n");
set_fwrite(stderr,imp_linset);
fprintf(stderr, "\n");
}
dd_FreeArow(d, cvec);
return imp_linset;
}
dd_boolean dd_MatrixCanonicalizeLinearity(dd_MatrixPtr *M, dd_rowset *impl_linset,dd_rowindex *newpos,
dd_ErrorType *error) /* 094 */
{
/* This is to recongnize all implicit linearities, and put all linearities at the top of
the matrix. All implicit linearities will be returned by *impl_linset.
*/
dd_rowrange rank;
dd_rowset linrows,ignoredrows,basisrows;
dd_colset ignoredcols,basiscols;
dd_rowrange i,k,m;
dd_rowindex newpos1;
dd_boolean success=dd_FALSE;
linrows=dd_ImplicitLinearityRows(*M, error);
if (*error!=dd_NoError) goto _L99;
m=(*M)->rowsize;
set_uni((*M)->linset, (*M)->linset, linrows);
/* add the implicit linrows to the explicit linearity rows */
/* To remove redundancy of the linearity part,
we need to compute the rank and a basis of the linearity part. */
set_initialize(&ignoredrows, (*M)->rowsize);
set_initialize(&ignoredcols, (*M)->colsize);
set_compl(ignoredrows, (*M)->linset);
rank=dd_MatrixRank(*M,ignoredrows,ignoredcols,&basisrows,&basiscols);
set_diff(ignoredrows, (*M)->linset, basisrows);
dd_MatrixRowsRemove2(M,ignoredrows,newpos);
dd_MatrixShiftupLinearity(M,&newpos1);
for (i=1; i<=m; i++){
k=(*newpos)[i];
if (k>0) {
(*newpos)[i]=newpos1[k];
}
}
*impl_linset=linrows;
success=dd_TRUE;
free(newpos1);
set_free(basisrows);
set_free(basiscols);
set_free(ignoredrows);
set_free(ignoredcols);
_L99:
return success;
}
dd_boolean dd_MatrixCanonicalize(dd_MatrixPtr *M, dd_rowset *impl_linset, dd_rowset *redset,
dd_rowindex *newpos, dd_ErrorType *error) /* 094 */
{
/* This is to find a canonical representation of a matrix *M by
recognizing all implicit linearities and all redundancies.
All implicit linearities will be returned by *impl_linset and
redundancies will be returned by *redset.
*/
dd_rowrange i,k,m;
dd_rowindex newpos1,revpos;
dd_rowset redset1;
dd_boolean success=dd_TRUE;
m=(*M)->rowsize;
set_initialize(redset, m);
revpos=(long *)calloc(m+1,sizeof(long));
success=dd_MatrixCanonicalizeLinearity(M, impl_linset, newpos, error);
if (!success) goto _L99;
for (i=1; i<=m; i++){
k=(*newpos)[i];
if (k>0) revpos[k]=i; /* inverse of *newpos[] */
}
success=dd_MatrixRedundancyRemove(M, &redset1, &newpos1, error); /* 094 */
if (!success) goto _L99;
for (i=1; i<=m; i++){
k=(*newpos)[i];
if (k>0) {
(*newpos)[i]=newpos1[k];
if (newpos1[k]<0) (*newpos)[i]=-revpos[-newpos1[k]]; /* update the certificate of its duplicate removal. */
if (set_member(k,redset1)) set_addelem(*redset, i);
}
}
_L99:
set_free(redset1);
free(newpos1);
free(revpos);
return success;
}
dd_boolean dd_ExistsRestrictedFace(dd_MatrixPtr M, dd_rowset R, dd_rowset S, dd_ErrorType *err)
/* 0.94 */
{
/* This function checkes if there is a point that satifies all the constraints of
the matrix M (interpreted as an H-representation) with additional equality contraints
specified by R and additional strict inequality constraints specified by S.
The set S is supposed to be disjoint from both R and M->linset. When it is not,
the set S will be considered as S\(R U M->linset).
*/
dd_boolean answer=dd_FALSE;
dd_LPPtr lp=NULL;
/*
printf("\n--- ERF ---\n");
printf("R = "); set_write(R);
printf("S = "); set_write(S);
*/
lp=dd_Matrix2Feasibility2(M, R, S, err);
if (*err!=dd_NoError) goto _L99;
/* Solve the LP by cdd LP solver. */
dd_LPSolve(lp, dd_DualSimplex, err); /* Solve the LP */
if (*err!=dd_NoError) goto _L99;
if (lp->LPS==dd_Optimal && dd_Positive(lp->optvalue)) {
answer=dd_TRUE;
}
dd_FreeLPData(lp);
_L99:
return answer;
}
dd_boolean dd_ExistsRestrictedFace2(dd_MatrixPtr M, dd_rowset R, dd_rowset S, dd_LPSolutionPtr *lps, dd_ErrorType *err)
/* 0.94 */
{
/* This function checkes if there is a point that satifies all the constraints of
the matrix M (interpreted as an H-representation) with additional equality contraints
specified by R and additional strict inequality constraints specified by S.
The set S is supposed to be disjoint from both R and M->linset. When it is not,
the set S will be considered as S\(R U M->linset).
This function returns a certificate of the answer in terms of the associated LP solutions.
*/
dd_boolean answer=dd_FALSE;
dd_LPPtr lp=NULL;
/*
printf("\n--- ERF ---\n");
printf("R = "); set_write(R);
printf("S = "); set_write(S);
*/
lp=dd_Matrix2Feasibility2(M, R, S, err);
if (*err!=dd_NoError) goto _L99;
/* Solve the LP by cdd LP solver. */
dd_LPSolve(lp, dd_DualSimplex, err); /* Solve the LP */
if (*err!=dd_NoError) goto _L99;
if (lp->LPS==dd_Optimal && dd_Positive(lp->optvalue)) {
answer=dd_TRUE;
}
(*lps)=dd_CopyLPSolution(lp);
dd_FreeLPData(lp);
_L99:
return answer;
}
dd_boolean dd_FindRelativeInterior(dd_MatrixPtr M, dd_rowset *ImL, dd_rowset *Lbasis, dd_LPSolutionPtr *lps, dd_ErrorType *err)
/* 0.94 */
{
/* This function computes a point in the relative interior of the H-polyhedron given by M.
Even the representation is V-representation, it simply interprete M as H-representation.
lps returns the result of solving an LP whose solution is a relative interior point.
ImL returns all row indices of M that are implicit linearities, i.e. their inqualities
are satisfied by equality by all points in the polyhedron. Lbasis returns a row basis
of the submatrix of M consisting of all linearities and implicit linearities. This means
that the dimension of the polyhedron is M->colsize - set_card(Lbasis) -1.
*/
dd_rowset S;
dd_colset T, Lbasiscols;
dd_boolean success=dd_FALSE;
dd_rowrange i;
dd_colrange rank;
*ImL=dd_ImplicitLinearityRows(M, err);
if (*err!=dd_NoError) goto _L99;
set_initialize(&S, M->rowsize); /* the empty set */
for (i=1; i <=M->rowsize; i++) {
if (!set_member(i, M->linset) && !set_member(i, *ImL)){
set_addelem(S, i); /* all nonlinearity rows go to S */
}
}
if (dd_ExistsRestrictedFace2(M, *ImL, S, lps, err)){
/* printf("a relative interior point found\n"); */
success=dd_TRUE;
}
set_initialize(&T, M->colsize); /* empty set */
rank=dd_MatrixRank(M,S,T,Lbasis,&Lbasiscols); /* the rank of the linearity submatrix of M. */
set_free(S);
set_free(T);
set_free(Lbasiscols);
_L99:
return success;
}
dd_rowrange dd_RayShooting(dd_MatrixPtr M, dd_Arow p, dd_Arow r)
{
/* 092, find the first inequality "hit" by a ray from an intpt. */
dd_rowrange imin=-1,i,m;
dd_colrange j, d;
dd_Arow vecmin, vec;
mytype min,t1,t2,alpha, t1min;
dd_boolean started=dd_FALSE;
dd_boolean localdebug=dd_FALSE;
m=M->rowsize;
d=M->colsize;
if (!dd_Equal(dd_one, p[0])){
fprintf(stderr, "Warning: RayShooting is called with a point with first coordinate not 1.\n");
dd_set(p[0],dd_one);
}
if (!dd_EqualToZero(r[0])){
fprintf(stderr, "Warning: RayShooting is called with a direction with first coordinate not 0.\n");
dd_set(r[0],dd_purezero);
}
dd_init(alpha); dd_init(min); dd_init(t1); dd_init(t2); dd_init(t1min);
dd_InitializeArow(d,&vecmin);
dd_InitializeArow(d,&vec);
for (i=1; i<=m; i++){
dd_InnerProduct(t1, d, M->matrix[i-1], p);
if (dd_Positive(t1)) {
dd_InnerProduct(t2, d, M->matrix[i-1], r);
dd_div(alpha, t2, t1);
if (!started){
imin=i; dd_set(min, alpha);
dd_set(t1min, t1); /* store the denominator. */
started=dd_TRUE;
if (localdebug) {
fprintf(stderr," Level 1: imin = %ld and min = ", imin);
dd_WriteNumber(stderr, min);
fprintf(stderr,"\n");
}
} else {
if (dd_Smaller(alpha, min)){
imin=i; dd_set(min, alpha);
dd_set(t1min, t1); /* store the denominator. */
if (localdebug) {
fprintf(stderr," Level 2: imin = %ld and min = ", imin);
dd_WriteNumber(stderr, min);
fprintf(stderr,"\n");
}
} else {
if (dd_Equal(alpha, min)) { /* tie break */
for (j=1; j<= d; j++){
dd_div(vecmin[j-1], M->matrix[imin-1][j-1], t1min);
dd_div(vec[j-1], M->matrix[i-1][j-1], t1);
}
if (dd_LexSmaller(vec,vecmin, d)){
imin=i; dd_set(min, alpha);
dd_set(t1min, t1); /* store the denominator. */
if (localdebug) {
fprintf(stderr," Level 3: imin = %ld and min = ", imin);
dd_WriteNumber(stderr, min);
fprintf(stderr,"\n");
}
}
}
}
}
}
}
dd_clear(alpha); dd_clear(min); dd_clear(t1); dd_clear(t2); dd_clear(t1min);
dd_FreeArow(d, vecmin);
dd_FreeArow(d, vec);
return imin;
}
#ifdef GMPRATIONAL
void dd_BasisStatusMaximize(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A,dd_Bmatrix T,dd_rowset equalityset,
dd_rowrange objrow,dd_colrange rhscol,ddf_LPStatusType LPS,
mytype *optvalue,dd_Arow sol,dd_Arow dsol,dd_rowset posset, ddf_colindex nbindex,
ddf_rowrange re,ddf_colrange se, dd_colrange *nse, long *pivots, int *found, int *LPScorrect)
/* This is just to check whether the status LPS of the basis given by
nbindex with extra certificates se or re is correct. It is done
by recomputing the basis inverse matrix T. It does not solve the LP
when the status *LPS is undecided. Thus the input is
m_size, d_size, A, equalityset, LPS, nbindex, re and se.
Other values will be recomputed from scratch.
The main purpose of the function is to verify the correctness
of the result of floating point computation with the GMP rational
arithmetics.
*/
{
long pivots0,pivots1,fbasisrank;
dd_rowrange i,is;
dd_colrange s,senew,j;
static dd_rowindex bflag;
static long mlast=0;
static dd_rowindex OrderVector; /* the permutation vector to store a preordered row indices */
unsigned int rseed=1;
mytype val;
dd_colindex nbtemp;
dd_LPStatusType ddlps;
dd_boolean localdebug=dd_FALSE;
if (dd_debug) localdebug=dd_debug;
if (localdebug){
printf("\nEvaluating dd_BasisStatusMaximize:\n");
}
dd_init(val);
nbtemp=(long *) calloc(d_size+1,sizeof(long));
for (i=0; i<= 4; i++) pivots[i]=0;
if (bflag==NULL || mlast!=m_size){
if (mlast!=m_size && mlast>0) {
free(bflag); /* called previously with different m_size */
free(OrderVector);
}
bflag=(long *) calloc(m_size+1,sizeof(long));
OrderVector=(long *)calloc(m_size+1,sizeof(long));
/* initialize only for the first time or when a larger space is needed */
mlast=m_size;
}
/* Initializing control variables. */
dd_ComputeRowOrderVector2(m_size,d_size,A,OrderVector,dd_MinIndex,rseed);
pivots1=0;
dd_ResetTableau(m_size,d_size,T,nbtemp,bflag,objrow,rhscol);
if (localdebug){
printf("\nnbindex:");
for (j=1; j<=d_size; j++) printf(" %ld", nbindex[j]);
printf("\n");
printf("re = %ld, se=%ld\n", re, se);
}
is=nbindex[se];
if (localdebug) printf("se=%ld, is=%ld\n", se, is);
fbasisrank=d_size-1;
for (j=1; j<=d_size; j++){
if (nbindex[j]<0) fbasisrank=fbasisrank-1;
/* fbasisrank=the basis rank computed by floating-point */
}
if (fbasisrank<d_size-1) {
if (localdebug) {
printf("d_size = %ld, the size of basis = %ld\n", d_size, fbasisrank);
printf("dd_BasisStatusMaximize: the size of basis is smaller than d-1.\nIt is safer to run the LP solver with GMP\n");
}
*found=dd_FALSE;
goto _L99;
/* Suspicious case. Rerun the LP solver with GMP. */
}
dd_FindLPBasis2(m_size,d_size,A,T,OrderVector, equalityset,nbindex,bflag,
objrow,rhscol,&s,found,&pivots0);
/* set up the new se column and corresponding variable */
senew=bflag[is];
is=nbindex[senew];
if (localdebug) printf("new se=%ld, is=%ld\n", senew, is);
pivots[4]=pivots0; /*GMP postopt pivots */
dd_statBSpivots+=pivots0;
if (!(*found)){
if (localdebug) {
printf("dd_BasisStatusMaximize: a specified basis DOES NOT exist.\n");
}
goto _L99;
/* No speficied LP basis is found. */
}
if (localdebug) {
printf("dd_BasisStatusMaximize: a specified basis exists.\n");
if (m_size <=100 && d_size <=30)
dd_WriteTableau(stdout,m_size,d_size,A,T,nbindex,bflag);
}
/* Check whether a recomputed basis is of the type specified by LPS */
*LPScorrect=dd_TRUE;
switch (LPS){
case dd_Optimal:
for (i=1; i<=m_size; i++) {
if (i!=objrow && bflag[i]==-1) { /* i is a basic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,i,rhscol);
if (dd_Negative(val)) {
if (localdebug) printf("RHS entry for %ld is negative\n", i);
*LPScorrect=dd_FALSE;
break;
}
} else if (bflag[i] >0) { /* i is nonbasic variable */
dd_TableauEntry(&val,m_size,d_size,A,T,objrow,bflag[i]);
if (dd_Positive(val)) {
if (localdebug) printf("Reduced cost entry for %ld is positive\n", i);
*LPScorrect=dd_FALSE;
break;
}
}
};
break;
case dd_Inconsistent:
for (j=1; j<=d_size; j++){
dd_TableauEntry(&val,m_size,d_size,A,T,re,j);
if (j==rhscol){
if (dd_Nonnegative(val)){
if (localdebug) printf("RHS entry for %ld is nonnegative\n", re);
*LPScorrect=dd_FALSE;
break;
}
} else if (dd_Positive(val)){
if (localdebug) printf("the row entry for(%ld, %ld) is positive\n", re, j);
*LPScorrect=dd_FALSE;
break;
}
};
break;
case dd_DualInconsistent:
for (i=1; i<=m_size; i++){
dd_TableauEntry(&val,m_size,d_size,A,T,i,bflag[is]);
if (i==objrow){
if (dd_Nonpositive(val)){
if (localdebug) printf("Reduced cost entry for %ld is nonpositive\n", bflag[is]);
*LPScorrect=dd_FALSE;
break;
}
} else if (dd_Negative(val)){
if (localdebug) printf("the column entry for(%ld, %ld) is positive\n", i, bflag[is]);
*LPScorrect=dd_FALSE;
break;
}
};
break;
;
default: break;
}
ddlps=LPSf2LPS(LPS);
dd_SetSolutions(m_size,d_size,A,T,
objrow,rhscol,ddlps,optvalue,sol,dsol,posset,nbindex,re,senew,bflag);
*nse=senew;
_L99:
dd_clear(val);
free(nbtemp);
}
void dd_BasisStatusMinimize(dd_rowrange m_size,dd_colrange d_size,
dd_Amatrix A,dd_Bmatrix T,dd_rowset equalityset,
dd_rowrange objrow,dd_colrange rhscol,ddf_LPStatusType LPS,
mytype *optvalue,dd_Arow sol,dd_Arow dsol, dd_rowset posset, ddf_colindex nbindex,
ddf_rowrange re,ddf_colrange se,dd_colrange *nse,long *pivots, int *found, int *LPScorrect)
{
dd_colrange j;
for (j=1; j<=d_size; j++) dd_neg(A[objrow-1][j-1],A[objrow-1][j-1]);
dd_BasisStatusMaximize(m_size,d_size,A,T,equalityset, objrow,rhscol,
LPS,optvalue,sol,dsol,posset,nbindex,re,se,nse,pivots,found,LPScorrect);
dd_neg(*optvalue,*optvalue);
for (j=1; j<=d_size; j++){
if (LPS!=dd_Inconsistent) {
/* Inconsistent certificate stays valid for minimization, 0.94e */
dd_neg(dsol[j-1],dsol[j-1]);
}
dd_neg(A[objrow-1][j-1],A[objrow-1][j-1]);
}
}
#endif
/* end of cddlp.c */