diff options
Diffstat (limited to '')
| -rw-r--r-- | sumofsquares/solver/scs.py | 34 |
1 files changed, 22 insertions, 12 deletions
diff --git a/sumofsquares/solver/scs.py b/sumofsquares/solver/scs.py index a476700..71c2248 100644 --- a/sumofsquares/solver/scs.py +++ b/sumofsquares/solver/scs.py @@ -37,7 +37,7 @@ def vec(m: NDArray) -> NDArray: """ n, _ = m.shape # Since it is symmetric we only work with the lower triangular part - scaling = (np.tril(n, k=-1) * np.sqrt(2) + np.eye(n)) + scaling = np.tril(np.ones(n), k=-1) * np.sqrt(2) + np.eye(n) v = (m * scaling)[np.tril_indices(n)].reshape((-1, 1)) assert v.shape[0] == n * (n + 1) // 2, "SCS Matrix vectorization is incorrect!" return v @@ -57,7 +57,7 @@ def mat(v: NDArray) -> NDArray: m = np.zeros((n, n)) m[np.tril_indices(n)] = v - scaling = (np.tril(n, k=-1) / np.sqrt(2) + np.eye(n)) + scaling = (np.tril(np.ones(n), k=-1) / np.sqrt(2) + np.eye(n)) m = m * scaling m = m + m.T - np.diag(np.diag(m)) return m @@ -87,11 +87,17 @@ def solve_cone(prob: ConicProblem, verbose: bool = False, # ep* dual exponential cone # p power cone # p* dual power cone + # + # In their documentation they call the linear coefficient c. Here we use q. P, q = None, None A_rows, b_rows = [], [] - # for (linear, constant) in prob.constrains["z"]: + if prob.q is not None: + q = prob.q + + if prob.constraints["z"]: + raise NotImplementedError if prob.constraints["l"]: raise NotImplementedError @@ -102,13 +108,15 @@ def solve_cone(prob: ConicProblem, verbose: bool = False, if prob.constraints["q"]: raise NotImplementedError - if prob.constraints["s"]: - raise NotImplementedError + for (linear, constant) in prob.constraints["s"]: + # -1 because RHS + b_rows.append(vec(constant) * -1) + A_rows.append(np.hstack(tuple(vec(m) for m in linear))) - if prob.constraints["ep"]: + if prob.constraints["e"]: raise NotImplementedError - if prob.constraints["ep*"]: + if prob.constraints["e*"]: raise NotImplementedError if prob.constraints["p"]: @@ -117,15 +125,16 @@ def solve_cone(prob: ConicProblem, verbose: bool = False, if prob.constraints["p*"]: raise NotImplementedError - assert len(A_rows) > 0, "Problem is unconstrained! Something is very wrong" + assert q is None or P is None or len(A_rows) > 0, "Problem is unconstrained! Something is very wrong" - A = np.hstack(A_rows) - b = np.hstack(b_rows) + A = np.vstack(A_rows) + b = np.vstack(b_rows) data = { "P": csc_matrix(P) if P is not None else None, - "q": q, - "A": csc_matrix(A), "b": b, + "c": q.reshape((-1,)), + "A": csc_matrix(A), + "b": b.reshape((-1,)), } cone = { @@ -155,6 +164,7 @@ def solve_cone(prob: ConicProblem, verbose: bool = False, results, i = {}, 0 for variable, shape in prob.variables.items(): num_indices = math.prod(shape) + # FIXME: need to restore shape using mat() values = np.array(sol["x"][i:i+num_indices]).reshape(shape) if values.shape == (1, 1): values = values[0, 0] |