diff --git a/y2014/control_loops/python/BUILD b/y2014/control_loops/python/BUILD
index 84e73a5..3802cb3 100644
--- a/y2014/control_loops/python/BUILD
+++ b/y2014/control_loops/python/BUILD
@@ -62,3 +62,15 @@
     '//frc971/control_loops/python:controls',
   ]
 )
+
+py_binary(
+  name = 'extended_lqr',
+  srcs = [
+    'extended_lqr.py',
+  ],
+  deps = [
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ],
+)
diff --git a/y2014/control_loops/python/extended_lqr.py b/y2014/control_loops/python/extended_lqr.py
new file mode 100755
index 0000000..095a43a
--- /dev/null
+++ b/y2014/control_loops/python/extended_lqr.py
@@ -0,0 +1,437 @@
+#!/usr/bin/python
+
+# This is an initial, hacky implementation of the extended LQR paper. It's just a proof of concept,
+# so don't trust it too much.
+
+import numpy
+import scipy.optimize
+from matplotlib import pylab
+import sys
+
+from frc971.control_loops.python import controls
+
+l = 100
+width = 0.2
+dt = 0.05
+num_states = 3
+num_inputs = 2
+x_hat_initial = numpy.matrix([[0.10], [1.0], [0.0]])
+
+def dynamics(X, U):
+  """Calculates the dynamics for a 2 wheeled robot.
+
+  Args:
+    X, numpy.matrix(3, 1), The state.  [x, y, theta]
+    U, numpy.matrix(2, 1), The input.  [left velocity, right velocity]
+
+  Returns:
+    numpy.matrix(3, 1), The derivative of the dynamics.
+  """
+  #return numpy.matrix([[X[1, 0]],
+  #                     [X[2, 0]],
+  #                     [U[0, 0]]])
+  return numpy.matrix([[(U[0, 0] + U[1, 0]) * numpy.cos(X[2, 0]) / 2.0],
+                       [(U[0, 0] + U[1, 0]) * numpy.sin(X[2, 0]) / 2.0],
+                       [(U[1, 0] - U[0, 0]) / width]])
+
+def RungeKutta(f, x, dt):
+  """4th order RungeKutta integration of F starting at X."""
+  a = f(x)
+  b = f(x + dt / 2.0 * a)
+  c = f(x + dt / 2.0 * b)
+  d = f(x + dt * c)
+  return x + dt * (a + 2.0 * b + 2.0 * c + d) / 6.0
+
+def discrete_dynamics(X, U):
+  return RungeKutta(lambda startingX: dynamics(startingX, U), X, dt)
+
+def inverse_discrete_dynamics(X, U):
+  return RungeKutta(lambda startingX: -dynamics(startingX, U), X, dt)
+
+def numerical_jacobian_x(fn, X, U, epsilon=1e-4):
+  """Numerically estimates the jacobian around X, U in X.
+
+  Args:
+    fn: A function of X, U.
+    X: numpy.matrix(num_states, 1), The state vector to take the jacobian
+      around.
+    U: numpy.matrix(num_inputs, 1), The input vector to take the jacobian
+      around.
+
+  Returns:
+    numpy.matrix(num_states, num_states), The jacobian of fn with X as the
+      variable.
+  """
+  num_states = X.shape[0]
+  nominal = fn(X, U)
+  answer = numpy.matrix(numpy.zeros((nominal.shape[0], num_states)))
+  # It's more expensive, but +- epsilon will be more reliable
+  for i in range(0, num_states):
+    dX_plus = X.copy()
+    dX_plus[i] += epsilon
+    dX_minus = X.copy()
+    dX_minus[i] -= epsilon
+    answer[:, i] = (fn(dX_plus, U) - fn(dX_minus, U)) / epsilon / 2.0
+  return answer
+
+def numerical_jacobian_u(fn, X, U, epsilon=1e-4):
+  """Numerically estimates the jacobian around X, U in U.
+
+  Args:
+    fn: A function of X, U.
+    X: numpy.matrix(num_states, 1), The state vector to take the jacobian
+      around.
+    U: numpy.matrix(num_inputs, 1), The input vector to take the jacobian
+      around.
+
+  Returns:
+    numpy.matrix(num_states, num_inputs), The jacobian of fn with U as the
+      variable.
+  """
+  num_states = X.shape[0]
+  num_inputs = U.shape[0]
+  nominal = fn(X, U)
+  answer = numpy.matrix(numpy.zeros((nominal.shape[0], num_inputs)))
+  for i in range(0, num_inputs):
+    dU_plus = U.copy()
+    dU_plus[i] += epsilon
+    dU_minus = U.copy()
+    dU_minus[i] -= epsilon
+    answer[:, i] = (fn(X, dU_plus) - fn(X, dU_minus)) / epsilon / 2.0
+  return answer
+
+def numerical_jacobian_x_x(fn, X, U):
+  return numerical_jacobian_x(
+      lambda X_inner, U_inner: numerical_jacobian_x(fn, X_inner, U_inner).T, X, U)
+
+def numerical_jacobian_x_u(fn, X, U):
+  return numerical_jacobian_x(
+      lambda X_inner, U_inner: numerical_jacobian_u(fn, X_inner, U_inner).T, X, U)
+
+def numerical_jacobian_u_x(fn, X, U):
+  return numerical_jacobian_u(
+      lambda X_inner, U_inner: numerical_jacobian_x(fn, X_inner, U_inner).T, X, U)
+
+def numerical_jacobian_u_u(fn, X, U):
+  return numerical_jacobian_u(
+      lambda X_inner, U_inner: numerical_jacobian_u(fn, X_inner, U_inner).T, X, U)
+
+# Simple implementation for a quadratic cost function.
+class CostFunction:
+  def __init__(self):
+    self.num_states = num_states
+    self.num_inputs = num_inputs
+    self.dt = dt
+    self.Q = numpy.matrix([[0.1, 0, 0],
+                           [0, 0.6, 0],
+                           [0, 0, 0.1]]) / dt / dt
+    self.R = numpy.matrix([[0.40, 0],
+                           [0, 0.40]]) / dt / dt
+
+  def estimate_Q_final(self, X_hat):
+    """Returns the quadraticized final Q around X_hat.
+
+    This is calculated by evaluating partial^2 cost(X_hat) / (partial X * partial X)
+
+    Args:
+      X_hat: numpy.matrix(self.num_states, 1), The state to quadraticize around.
+
+    Result:
+      numpy.matrix(self.num_states, self.num_states)
+    """
+    zero_U = numpy.matrix(numpy.zeros((self.num_inputs, 1)))
+    return numerical_jacobian_x_x(self.final_cost, X_hat, zero_U)
+
+  def estimate_partial_cost_partial_x_final(self, X_hat):
+    """Returns \frac{\partial cost}{\partial X}(X_hat) for the final cost.
+
+    Args:
+      X_hat: numpy.matrix(self.num_states, 1), The state to quadraticize around.
+
+    Result:
+      numpy.matrix(self.num_states, 1)
+    """
+    return numerical_jacobian_x(self.final_cost, X_hat, numpy.matrix(numpy.zeros((self.num_inputs, 1)))).T
+
+  def estimate_q_final(self, X_hat):
+    """Returns q evaluated at X_hat for the final cost function."""
+    return self.estimate_partial_cost_partial_x_final(X_hat) - self.estimate_Q_final(X_hat) * X_hat
+
+  def final_cost(self, X, U):
+    """Computes the final cost of being at X
+
+    Args:
+      X: numpy.matrix(self.num_states, 1)
+      U: numpy.matrix(self.num_inputs, 1), ignored
+
+    Returns:
+      numpy.matrix(1, 1), The quadratic cost of being at X
+    """
+    return X.T * self.Q * X * 1000
+
+  def cost(self, X, U):
+    """Computes the incremental cost given a position and U.
+
+    Args:
+      X: numpy.matrix(self.num_states, 1)
+      U: numpy.matrix(self.num_inputs, 1)
+
+    Returns:
+      numpy.matrix(1, 1), The quadratic cost of evaluating U.
+    """
+    return U.T * self.R * U + X.T * self.Q * X
+
+cost_fn_obj = CostFunction()
+
+S_bar_t = [numpy.matrix(numpy.zeros((num_states, num_states))) for _ in range(l + 1)]
+s_bar_t = [numpy.matrix(numpy.zeros((num_states, 1))) for _ in range(l + 1)]
+s_scalar_bar_t = [numpy.matrix(numpy.zeros((1, 1))) for _ in range(l + 1)]
+
+L_t = [numpy.matrix(numpy.zeros((num_inputs, num_states))) for _ in range(l + 1)]
+l_t = [numpy.matrix(numpy.zeros((num_inputs, 1))) for _ in range(l + 1)]
+L_bar_t = [numpy.matrix(numpy.zeros((num_inputs, num_states))) for _ in range(l + 1)]
+l_bar_t = [numpy.matrix(numpy.zeros((num_inputs, 1))) for _ in range(l + 1)]
+
+S_t = [numpy.matrix(numpy.zeros((num_states, num_states))) for _ in range(l + 1)]
+s_t = [numpy.matrix(numpy.zeros((num_states, 1))) for _ in range(l + 1)]
+s_scalar_t = [numpy.matrix(numpy.zeros((1, 1))) for _ in range(l + 1)]
+
+
+last_x_hat_t = [numpy.matrix(numpy.zeros((num_states, 1))) for _ in range(l + 1)]
+
+for a in range(15):
+  x_hat = x_hat_initial
+  u_t = L_t[0] * x_hat + l_t[0]
+  S_bar_t[0] = numpy.matrix(numpy.zeros((num_states, num_states)))
+  s_bar_t[0] = numpy.matrix(numpy.zeros((num_states, 1)))
+  s_scalar_bar_t[0] = numpy.matrix([[0]])
+
+  last_x_hat_t[0] = x_hat_initial
+
+  Q_t = numerical_jacobian_x_x(cost_fn_obj.cost, x_hat_initial, u_t)
+  P_t = numerical_jacobian_x_u(cost_fn_obj.cost, x_hat_initial, u_t)
+  R_t = numerical_jacobian_u_u(cost_fn_obj.cost, x_hat_initial, u_t)
+
+  q_t = numerical_jacobian_x(cost_fn_obj.cost, x_hat_initial, u_t).T - Q_t * x_hat_initial - P_t.T * u_t
+  r_t = numerical_jacobian_u(cost_fn_obj.cost, x_hat_initial, u_t).T - P_t * x_hat_initial - R_t * u_t
+
+  q_scalar_t = cost_fn_obj.cost(x_hat_initial, u_t) - 0.5 * (x_hat_initial.T * (Q_t * x_hat_initial + P_t.T * u_t) + u_t.T * (P_t * x_hat_initial + R_t * u_t)) - x_hat_initial.T * q_t - u_t.T * r_t
+
+  start_A_t = numerical_jacobian_x(discrete_dynamics, x_hat_initial, u_t)
+  start_B_t = numerical_jacobian_u(discrete_dynamics, x_hat_initial, u_t)
+  x_hat_next = discrete_dynamics(x_hat_initial, u_t)
+  start_c_t = x_hat_next - start_A_t * x_hat_initial - start_B_t * u_t
+
+  B_svd_u, B_svd_sigma_diag, B_svd_v = numpy.linalg.svd(start_B_t)
+  B_svd_sigma = numpy.matrix(numpy.zeros(start_B_t.shape))
+  B_svd_sigma[0:B_svd_sigma_diag.shape[0], 0:B_svd_sigma_diag.shape[0]] = numpy.diag(B_svd_sigma_diag)
+
+  B_svd_sigma_inv = numpy.matrix(numpy.zeros(start_B_t.shape)).T
+  B_svd_sigma_inv[0:B_svd_sigma_diag.shape[0], 0:B_svd_sigma_diag.shape[0]] = numpy.linalg.inv(numpy.diag(B_svd_sigma_diag))
+  B_svd_inv = B_svd_v.T * B_svd_sigma_inv * B_svd_u.T
+
+  L_bar_t[1] = B_svd_inv
+  l_bar_t[1] = -B_svd_inv * (start_A_t * x_hat_initial + start_c_t)
+
+  S_bar_t[1] = L_bar_t[1].T * R_t * L_bar_t[1]
+
+  TotalS_1 = start_B_t.T * S_t[1] * start_B_t + R_t
+  Totals_1 = start_B_t.T * S_t[1] * (start_c_t + start_A_t * x_hat_initial) + start_B_t.T * s_t[1] + P_t * x_hat_initial + r_t
+  Totals_scalar_1 = 0.5 * (start_c_t.T + x_hat_initial.T * start_A_t.T) * S_t[1] * (start_c_t + start_A_t * x_hat_initial) + s_scalar_t[1] + x_hat_initial.T * q_t + q_scalar_t + 0.5 * x_hat_initial.T * Q_t * x_hat_initial + (start_c_t.T + x_hat_initial.T * start_A_t.T) * s_t[1]
+
+  optimal_u_1 = -numpy.linalg.solve(TotalS_1, Totals_1)
+  optimal_x_1 = start_A_t * x_hat_initial + start_B_t * optimal_u_1 + start_c_t
+
+  S_bar_1_eigh_eigenvalues, S_bar_1_eigh_eigenvectors = numpy.linalg.eigh(S_bar_t[1])
+  S_bar_1_eigh = numpy.matrix(numpy.diag(S_bar_1_eigh_eigenvalues))
+  S_bar_1_eigh_eigenvalues_stiff = S_bar_1_eigh_eigenvalues.copy()
+  for i in range(S_bar_1_eigh_eigenvalues_stiff.shape[0]):
+    if abs(S_bar_1_eigh_eigenvalues_stiff[i]) < 1e-8:
+      S_bar_1_eigh_eigenvalues_stiff[i] = max(S_bar_1_eigh_eigenvalues_stiff) * 1.0
+
+  #print 'eigh eigenvalues of S bar', S_bar_1_eigh_eigenvalues
+  #print 'eigh eigenvectors of S bar', S_bar_1_eigh_eigenvectors.T
+
+  #print 'S bar eig recreate', S_bar_1_eigh_eigenvectors * numpy.matrix(numpy.diag(S_bar_1_eigh_eigenvalues)) * S_bar_1_eigh_eigenvectors.T
+  #print 'S bar eig recreate error', (S_bar_1_eigh_eigenvectors * numpy.matrix(numpy.diag(S_bar_1_eigh_eigenvalues)) * S_bar_1_eigh_eigenvectors.T - S_bar_t[1])
+
+  S_bar_stiff = S_bar_1_eigh_eigenvectors * numpy.matrix(numpy.diag(S_bar_1_eigh_eigenvalues_stiff)) * S_bar_1_eigh_eigenvectors.T
+
+  print 'Min u', -numpy.linalg.solve(TotalS_1, Totals_1)
+  print 'Min x_hat', optimal_x_1
+  s_bar_t[1] = -s_t[1] - (S_bar_stiff + S_t[1]) * optimal_x_1
+  s_scalar_bar_t[1] = 0.5 * (optimal_u_1.T * TotalS_1 * optimal_u_1 - optimal_x_1.T * (S_bar_stiff + S_t[1]) * optimal_x_1) + optimal_u_1.T * Totals_1 - optimal_x_1.T * (s_bar_t[1] + s_t[1]) - s_scalar_t[1] + Totals_scalar_1
+
+  print 'optimal_u_1', optimal_u_1
+  print 'TotalS_1', TotalS_1
+  print 'Totals_1', Totals_1
+  print 'Totals_scalar_1', Totals_scalar_1
+  print 'overall cost 1',  0.5 * (optimal_u_1.T * TotalS_1 * optimal_u_1) + optimal_u_1.T * Totals_1 + Totals_scalar_1
+  print 'overall cost 0',  0.5 * (x_hat_initial.T * S_t[0] * x_hat_initial) +  x_hat_initial.T * s_t[0] + s_scalar_t[0]
+
+  print 't forward 0'
+  print 'x_hat_initial[ 0]: %s' % (x_hat_initial)
+  print 'x_hat[%2d]: %s' % (0, x_hat.T)
+  print 'x_hat_next[%2d]: %s' % (0, x_hat_next.T)
+  print 'u[%2d]: %s' % (0, u_t.T)
+  print ('L[ 0]: %s' % (L_t[0],)).replace('\n', '\n        ')
+  print ('l[ 0]: %s' % (l_t[0],)).replace('\n', '\n        ')
+
+  print ('A_t[%2d]: %s' % (0, start_A_t)).replace('\n', '\n         ')
+  print ('B_t[%2d]: %s' % (0, start_B_t)).replace('\n', '\n         ')
+  print ('c_t[%2d]: %s' % (0, start_c_t)).replace('\n', '\n         ')
+
+  # TODO(austin): optimal_x_1 is x_hat
+  x_hat = -numpy.linalg.solve((S_t[1] + S_bar_stiff), (s_t[1] + s_bar_t[1]))
+  print 'new xhat', x_hat
+
+  S_bar_t[1] = S_bar_stiff
+
+  last_x_hat_t[1] = x_hat
+
+  for t in range(1, l):
+    print 't forward', t
+    u_t = L_t[t] * x_hat + l_t[t]
+
+    x_hat_next = discrete_dynamics(x_hat, u_t)
+    A_bar_t = numerical_jacobian_x(inverse_discrete_dynamics, x_hat_next, u_t)
+    B_bar_t = numerical_jacobian_u(inverse_discrete_dynamics, x_hat_next, u_t)
+    c_bar_t = x_hat - A_bar_t * x_hat_next - B_bar_t * u_t
+
+    print 'x_hat[%2d]: %s' % (t, x_hat.T)
+    print 'x_hat_next[%2d]: %s' % (t, x_hat_next.T)
+    print ('L[%2d]: %s' % (t, L_t[t],)).replace('\n', '\n        ')
+    print ('l[%2d]: %s' % (t, l_t[t],)).replace('\n', '\n        ')
+    print 'u[%2d]: %s' % (t, u_t.T)
+
+    print ('A_bar_t[%2d]: %s' % (t, A_bar_t)).replace('\n', '\n             ')
+    print ('B_bar_t[%2d]: %s' % (t, B_bar_t)).replace('\n', '\n             ')
+    print ('c_bar_t[%2d]: %s' % (t, c_bar_t)).replace('\n', '\n             ')
+
+    Q_t = numerical_jacobian_x_x(cost_fn_obj.cost, x_hat, u_t)
+    P_t = numerical_jacobian_x_u(cost_fn_obj.cost, x_hat, u_t)
+    R_t = numerical_jacobian_u_u(cost_fn_obj.cost, x_hat, u_t)
+
+    q_t = numerical_jacobian_x(cost_fn_obj.cost, x_hat, u_t).T - Q_t * x_hat - P_t.T * u_t
+    r_t = numerical_jacobian_u(cost_fn_obj.cost, x_hat, u_t).T - P_t * x_hat - R_t * u_t
+
+    q_scalar_t = cost_fn_obj.cost(x_hat, u_t) - 0.5 * (x_hat.T * (Q_t * x_hat + P_t.T * u_t) + u_t.T * (P_t * x_hat + R_t * u_t)) - x_hat.T * q_t - u_t.T * r_t
+
+    C_bar_t = B_bar_t.T * (S_bar_t[t] + Q_t) * A_bar_t + P_t * A_bar_t
+    D_bar_t = A_bar_t.T * (S_bar_t[t] + Q_t) * A_bar_t
+    E_bar_t = B_bar_t.T * (S_bar_t[t] + Q_t) * B_bar_t + R_t + P_t * B_bar_t + B_bar_t.T * P_t.T
+    d_bar_t = A_bar_t.T * (s_bar_t[t] + q_t) + A_bar_t.T * (S_bar_t[t] + Q_t) * c_bar_t
+    e_bar_t = r_t + P_t * c_bar_t + B_bar_t.T * s_bar_t[t] + B_bar_t.T * (S_bar_t[t] + Q_t) * c_bar_t
+
+    L_bar_t[t + 1] = -numpy.linalg.inv(E_bar_t) * C_bar_t
+    l_bar_t[t + 1] = -numpy.linalg.inv(E_bar_t) * e_bar_t
+
+    S_bar_t[t + 1] = D_bar_t + C_bar_t.T * L_bar_t[t + 1]
+    s_bar_t[t + 1] = d_bar_t + C_bar_t.T * l_bar_t[t + 1]
+    s_scalar_bar_t[t + 1] = -0.5 * e_bar_t.T * numpy.linalg.inv(E_bar_t) * e_bar_t + 0.5 * c_bar_t.T * (S_bar_t[t] + Q_t) * c_bar_t + c_bar_t.T * s_bar_t[t] + c_bar_t.T * q_t + s_scalar_bar_t[t] + q_scalar_t
+
+    x_hat = -numpy.linalg.solve((S_t[t + 1] + S_bar_t[t + 1]), (s_t[t + 1] + s_bar_t[t + 1]))
+
+  S_t[l] = cost_fn_obj.estimate_Q_final(x_hat)
+  s_t[l] = cost_fn_obj.estimate_q_final(x_hat)
+  x_hat = -numpy.linalg.inv(S_t[l] + S_bar_t[l]) * (s_t[l] + s_bar_t[l])
+
+  for t in reversed(range(l)):
+    print 't backward', t
+    # TODO(austin): I don't think we can use L_t like this here.
+    # I think we are off by 1 somewhere...
+    u_t = L_bar_t[t + 1] * x_hat + l_bar_t[t + 1]
+
+    x_hat_prev = inverse_discrete_dynamics(x_hat, u_t)
+    print 'x_hat[%2d]: %s' % (t, x_hat.T)
+    print 'x_hat_prev[%2d]: %s' % (t, x_hat_prev.T)
+    print ('L_bar[%2d]: %s' % (t + 1, L_bar_t[t + 1])).replace('\n', '\n            ')
+    print ('l_bar[%2d]: %s' % (t + 1, l_bar_t[t + 1])).replace('\n', '\n            ')
+    print 'u[%2d]: %s' % (t, u_t.T)
+    # Now compute the linearized A, B, and C
+    # Start by doing it numerically, and then optimize.
+    A_t = numerical_jacobian_x(discrete_dynamics, x_hat_prev, u_t)
+    B_t = numerical_jacobian_u(discrete_dynamics, x_hat_prev, u_t)
+    c_t = x_hat - A_t * x_hat_prev - B_t * u_t
+
+    print ('A_t[%2d]: %s' % (t, A_t)).replace('\n', '\n         ')
+    print ('B_t[%2d]: %s' % (t, B_t)).replace('\n', '\n         ')
+    print ('c_t[%2d]: %s' % (t, c_t)).replace('\n', '\n         ')
+
+    Q_t = numerical_jacobian_x_x(cost_fn_obj.cost, x_hat_prev, u_t)
+    P_t = numerical_jacobian_x_u(cost_fn_obj.cost, x_hat_prev, u_t)
+    P_T_t = numerical_jacobian_u_x(cost_fn_obj.cost, x_hat_prev, u_t)
+    R_t = numerical_jacobian_u_u(cost_fn_obj.cost, x_hat_prev, u_t)
+
+    q_t = numerical_jacobian_x(cost_fn_obj.cost, x_hat_prev, u_t).T - Q_t * x_hat_prev - P_T_t * u_t
+    r_t = numerical_jacobian_u(cost_fn_obj.cost, x_hat_prev, u_t).T - P_t * x_hat_prev - R_t * u_t
+
+    q_scalar_t = cost_fn_obj.cost(x_hat_prev, u_t) - 0.5 * (x_hat_prev.T * (Q_t * x_hat_prev + P_t.T * u_t) + u_t.T * (P_t * x_hat_prev + R_t * u_t)) - x_hat_prev.T * q_t - u_t.T * r_t
+
+    C_t = P_t + B_t.T * S_t[t + 1] * A_t
+    D_t = Q_t + A_t.T * S_t[t + 1] * A_t
+    E_t = R_t + B_t.T * S_t[t + 1] * B_t
+    d_t = q_t + A_t.T * s_t[t + 1] + A_t.T * S_t[t + 1] * c_t
+    e_t = r_t + B_t.T * s_t[t + 1] + B_t.T * S_t[t + 1] * c_t
+    L_t[t] = -numpy.linalg.inv(E_t) * C_t
+    l_t[t] = -numpy.linalg.inv(E_t) * e_t
+    s_t[t] = d_t + C_t.T * l_t[t]
+    S_t[t] = D_t + C_t.T * L_t[t]
+    s_scalar_t[t] = q_scalar_t - 0.5 * e_t.T * numpy.linalg.inv(E_t) * e_t + 0.5 * c_t.T * S_t[t + 1] * c_t + c_t.T * s_t[t + 1] + s_scalar_t[t + 1]
+
+    x_hat = -numpy.linalg.solve((S_t[t] + S_bar_t[t]), (s_t[t] + s_bar_t[t]))
+    if t == 0:
+      last_x_hat_t[t] = x_hat_initial
+    else:
+      last_x_hat_t[t] = x_hat
+
+  x_hat_t = [x_hat_initial]
+
+  pylab.figure('states %d' % a)
+  pylab.ion()
+  for dim in range(num_states):
+    pylab.plot(numpy.arange(len(last_x_hat_t)),
+             [x_hat_loop[dim, 0] for x_hat_loop in last_x_hat_t], marker='o', label='Xhat[%d]'%dim)
+  pylab.legend()
+  pylab.draw()
+
+  pylab.figure('xy %d' % a)
+  pylab.ion()
+  pylab.plot([x_hat_loop[0, 0] for x_hat_loop in last_x_hat_t],
+             [x_hat_loop[1, 0] for x_hat_loop in last_x_hat_t], marker='o', label='trajectory')
+  pylab.legend()
+  pylab.draw()
+
+final_u_t = [numpy.matrix(numpy.zeros((num_inputs, 1))) for _ in range(l + 1)]
+cost_to_go = []
+cost_to_come = []
+cost = []
+for t in range(l):
+  cost_to_go.append((0.5 * last_x_hat_t[t].T * S_t[t] * last_x_hat_t[t] + last_x_hat_t[t].T * s_t[t] + s_scalar_t[t])[0, 0])
+  cost_to_come.append((0.5 * last_x_hat_t[t].T * S_bar_t[t] * last_x_hat_t[t] + last_x_hat_t[t].T * s_bar_t[t] + s_scalar_bar_t[t])[0, 0])
+  cost.append(cost_to_go[-1] + cost_to_come[-1])
+  final_u_t[t] = L_t[t] * last_x_hat_t[t] + l_t[t]
+
+for t in range(l):
+  A_t = numerical_jacobian_x(discrete_dynamics, last_x_hat_t[t], final_u_t[t])
+  B_t = numerical_jacobian_u(discrete_dynamics, last_x_hat_t[t], final_u_t[t])
+  c_t = discrete_dynamics(last_x_hat_t[t], final_u_t[t]) - A_t * last_x_hat_t[t] - B_t * final_u_t[t]
+  print("Infeasability at", t, "is", ((A_t * last_x_hat_t[t] + B_t * final_u_t[t] + c_t) - last_x_hat_t[t + 1]).T)
+
+pylab.figure('u')
+samples = numpy.arange(len(final_u_t))
+for i in range(num_inputs):
+  pylab.plot(samples, [u[i, 0] for u in final_u_t], label='u[%d]' % i, marker='o')
+  pylab.legend()
+
+pylab.figure('cost')
+cost_samples = numpy.arange(len(cost))
+pylab.plot(cost_samples, cost_to_go, label='cost to go', marker='o')
+pylab.plot(cost_samples, cost_to_come, label='cost to come', marker='o')
+pylab.plot(cost_samples, cost, label='cost', marker='o')
+pylab.legend()
+
+pylab.ioff()
+pylab.show()
+
+sys.exit(1)