diff options
Diffstat (limited to '')
| -rw-r--r-- | sumofsquares/solver/cvxopt.py | 36 |
1 files changed, 21 insertions, 15 deletions
diff --git a/sumofsquares/solver/cvxopt.py b/sumofsquares/solver/cvxopt.py index 0e5ce37..c4e1db3 100644 --- a/sumofsquares/solver/cvxopt.py +++ b/sumofsquares/solver/cvxopt.py @@ -3,6 +3,7 @@ Solve sumofsquares problems using CVXOPT """ import cvxopt import numpy as np +import math from collections import UserDict from numpy.typing import NDArray @@ -65,17 +66,15 @@ def solve_sos_cone(prob: Problem, verbose: bool = False, G_rows, h_rows= [], [] l_dims, q_dims, s_dims = [], [], [] - # Collect variable indices in dictionary and sort them by value - # i.e. we want variable_indices.values() to be sorted - variable_indices: dict[OptVariable, VariableIndex] = dict(sorted({ - # FIXME there can be more than one indices associated to v - v: next(prob.state.get_indices_as_variable_index(v)) - for v in prob.variables - }.items(), key=lambda item: item[1])) + # indices of variables in the optimization problem + variable_indices = sum(sorted(tuple(prob.state.get_indices_as_variable_index(v)) + for v in prob.variables), ()) # cost linear term - cost_coeffs = dict(cost.affine_coefficients_by_degrees(variable_indices.values())) - q = cost_coeffs[1].T + cost_coeffs = dict(cost.affine_coefficients_by_degrees(variable_indices)) + q = cost_coeffs.get(1) + if q is None: + q = np.zeros((len(variable_indices), 1)) # cost quadratic term if is_qp: @@ -114,10 +113,9 @@ def solve_sos_cone(prob: Problem, verbose: bool = False, # Linear term l_stacked_rows = [] - for v in variable_indices.values(): + for v in variable_indices: # Get the affine coefficient associated to this variable - linear = MonomialIndex((v,)) - v_coeff = constr.affine_coefficient(linear) + v_coeff = constr.affine_coefficient(v) # stack the columns of coeff matrix into a fat row vector (column major order). v_stacked = v_coeff.T.reshape((nrows * ncols,)) @@ -145,7 +143,7 @@ def solve_sos_cone(prob: Problem, verbose: bool = False, "into the canonical form of CVXOPT.") # Convert to CVXOPT matrices - q = cvxopt.matrix(q) + q = cvxopt.matrix(q.T) G = cvxopt.matrix(np.hstack(G_rows).T) if G_rows else None h = cvxopt.matrix(np.hstack(h_rows)) if h_rows else None A = cvxopt.matrix(np.hstack(A_rows).T) if A_rows else None @@ -177,7 +175,15 @@ def solve_sos_cone(prob: Problem, verbose: bool = False, *args, **kwargs) results = {} - for i, v in enumerate(variable_indices.keys()): - results[v] = info['x'][i] + + i = 0 + for variable in prob.variables: + num_indices = math.prod(variable.shape) + values = np.array(info['x'][i:i+num_indices]).reshape(variable.shape) + if values.shape == (1, 1): + values = values[0, 0] + + results[variable] = values + i += num_indices return results, CVXOPTInfo(info) |