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PANOC-ALM
quadratic-penalty
Nonconvex constrained optimization
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Go to the source code of this file.
Namespaces | |
| rosenbrock | |
Variables | |
| string | name = "minimal_example" |
| x = cs.SX.sym("x") | |
| y = cs.SX.sym("y") | |
| p = cs.SX.sym("p") | |
| tuple | cost = (1 - x) ** 2 + p * (y - x ** 2) ** 2 |
| tuple | constraint_g_cubic = (x - 1) ** 3 - y + 1 |
| int | constraint_g_linear = x + y - 2 |
| X = cs.vertcat(x, y) | |
| cost_function = cs.Function("f", [X, p], [cost]) | |
| g = cs.vertcat(constraint_g_cubic, constraint_g_linear) | |
| g_function = cs.Function("g", [X, p], [g]) | |
| cgen | |
| n | |
| m | |
| num_p | |
| prob = pa.compile_and_load_problem(cgen, n, m, num_p, name) | |
| lowerbound | |
| upperbound | |
| innersolver = pa.StructuredPANOCLBFGSSolver() | |
| solver = pa.ALMSolver(pa.ALMParams(), innersolver) | |
| param | |
| x_sol = np.array([1.0, 2.0]) | |
| y_sol = np.zeros((m,)) | |
| stats | |
| Y | |
| tuple | Z = (1 - X) ** 2 + 100 * (Y - X ** 2) ** 2 |
| color | |
| label | |
| marker | |