Squashed 'third_party/osqp/' content from commit 33454b3e23
Change-Id: I056df0582ca06664e86554c341a94c47ab932001
git-subtree-dir: third_party/osqp
git-subtree-split: 33454b3e236f1f44193bfbbb6b8c8e71f8f04e9a
Signed-off-by: Austin Schuh <austin.linux@gmail.com>
diff --git a/tests/update_matrices/generate_problem.py b/tests/update_matrices/generate_problem.py
new file mode 100644
index 0000000..d801fc7
--- /dev/null
+++ b/tests/update_matrices/generate_problem.py
@@ -0,0 +1,109 @@
+import numpy as np
+from scipy import sparse
+import utils.codegen_utils as cu
+from numpy.random import Generator, PCG64
+
+# Set random seed for reproducibility
+rg = Generator(PCG64(2))
+
+# Define tests
+n = 5
+m = 8
+test_form_KKT_n = n
+test_form_KKT_m = m
+p = 0.7
+
+test_form_KKT_A = sparse.random(test_form_KKT_m, test_form_KKT_n, density=p, format='csc', random_state=rg)
+test_form_KKT_P = sparse.random(n, n, density=p, random_state=rg)
+test_form_KKT_P = (test_form_KKT_P @ test_form_KKT_P.T).tocsc() + sparse.eye(n, format='csc')
+test_form_KKT_Pu = sparse.triu(test_form_KKT_P, format='csc')
+test_form_KKT_rho = 1.6
+test_form_KKT_sigma = 0.1
+test_form_KKT_KKT = sparse.bmat([[test_form_KKT_P + test_form_KKT_sigma *
+ sparse.eye(test_form_KKT_n), test_form_KKT_A.T],
+ [test_form_KKT_A, -1./test_form_KKT_rho *
+ sparse.eye(test_form_KKT_m)]], format='csc')
+test_form_KKT_KKTu = sparse.triu(test_form_KKT_KKT, format='csc')
+
+
+# Create new P, A and KKT
+test_form_KKT_A_new = test_form_KKT_A.copy()
+test_form_KKT_A_new.data += rg.standard_normal(test_form_KKT_A_new.nnz)
+test_form_KKT_Pu_new = test_form_KKT_Pu.copy()
+test_form_KKT_Pu_new.data += 0.1 * rg.standard_normal(test_form_KKT_Pu_new.nnz)
+test_form_KKT_P_new = test_form_KKT_Pu_new + test_form_KKT_Pu_new.T - sparse.diags(test_form_KKT_Pu_new.diagonal())
+
+test_form_KKT_KKT_new = sparse.bmat([[test_form_KKT_P_new + test_form_KKT_sigma *
+ sparse.eye(test_form_KKT_n), test_form_KKT_A_new.T],
+ [test_form_KKT_A_new, -1./test_form_KKT_rho *
+ sparse.eye(test_form_KKT_m)]], format='csc')
+test_form_KKT_KKTu_new = sparse.triu(test_form_KKT_KKT_new, format='csc')
+
+
+# Test solve problem with initial P and A
+test_solve_P = test_form_KKT_P.copy()
+test_solve_Pu = test_form_KKT_Pu.copy()
+test_solve_q = rg.standard_normal(n)
+test_solve_A = test_form_KKT_A.copy()
+test_solve_l = -30 + rg.standard_normal(m)
+test_solve_u = 30 + rg.standard_normal(m)
+
+
+# Define new P
+test_solve_P_new = test_form_KKT_P_new.copy()
+test_solve_Pu_new = test_form_KKT_Pu_new.copy()
+
+
+# Define new A
+test_solve_A_new = test_form_KKT_A_new.copy()
+
+
+# Generate test data and solutions
+data = {'test_form_KKT_n': test_form_KKT_n,
+ 'test_form_KKT_m': test_form_KKT_m,
+ 'test_form_KKT_A': test_form_KKT_A,
+ 'test_form_KKT_Pu': test_form_KKT_Pu,
+ 'test_form_KKT_rho': test_form_KKT_rho,
+ 'test_form_KKT_sigma': test_form_KKT_sigma,
+ 'test_form_KKT_KKT': test_form_KKT_KKT,
+ 'test_form_KKT_KKTu': test_form_KKT_KKTu,
+ 'test_form_KKT_A_new': test_form_KKT_A_new,
+ 'test_form_KKT_Pu_new': test_form_KKT_Pu_new,
+ 'test_form_KKT_KKT_new': test_form_KKT_KKT_new,
+ 'test_form_KKT_KKTu_new': test_form_KKT_KKTu_new,
+ 'test_solve_Pu': test_solve_Pu,
+ 'test_solve_q': test_solve_q,
+ 'test_solve_A': test_solve_A,
+ 'test_solve_l': test_solve_l,
+ 'test_solve_u': test_solve_u,
+ 'n': n,
+ 'm': m,
+ 'test_solve_x': np.array([-4.61725223e-01, 7.97298788e-01,
+ 5.55470173e-04, 3.37603740e-01,
+ -1.14060693e+00]),
+ 'test_solve_y': np.zeros(m),
+ 'test_solve_obj_value': -1.885431747787806,
+ 'test_solve_status': 'optimal',
+ 'test_solve_Pu_new': test_solve_Pu_new,
+ 'test_solve_P_new_x': np.array([-0.48845963, 0.70997599, -0.09017696,
+ 0.33176037, -1.01867464]),
+ 'test_solve_P_new_y': np.zeros(m),
+ 'test_solve_P_new_obj_value': -1.7649689689774013,
+ 'test_solve_P_new_status': 'optimal',
+ 'test_solve_A_new': test_solve_A_new,
+ 'test_solve_A_new_x': np.array([-4.61725223e-01, 7.97298788e-01,
+ 5.55470173e-04, 3.37603740e-01,
+ -1.14060693e+00]),
+ 'test_solve_A_new_y': np.zeros(m),
+ 'test_solve_A_new_obj_value': -1.8854317477878062,
+ 'test_solve_A_new_status': 'optimal',
+ 'test_solve_P_A_new_x': np.array([-0.48845963, 0.70997599, -0.09017696,
+ 0.33176037, -1.01867464]),
+ 'test_solve_P_A_new_y': np.zeros(m),
+ 'test_solve_P_A_new_obj_value': -1.764968968977401,
+ 'test_solve_P_A_new_status': 'optimal'
+ }
+
+
+# Generate test data
+cu.generate_data('update_matrices', data)