Method get_problem_data

cvxpy/problems/problem.py:703–900

Returns the problem data used in the call to the solver. When a problem is solved, CVXPY creates a chain of reductions enclosed in a :class:`~cvxpy.reductions.solvers.solving_chain.SolvingChain`, and compiles it to some low-level representation that is compatible wit

(
        self, solver,
        gp: bool = False,
        enforce_dpp: bool = False,
        ignore_dpp: bool = False,
        verbose: bool = False,
        canon_backend: str | None = None,
        solver_opts: dict | None = None,
    )

        cls.REGISTERED_SOLVE_METHODS[name] = func

    def get_problem_data(
        self, solver,
        gp: bool = False,
        enforce_dpp: bool = False,
        ignore_dpp: bool = False,
        verbose: bool = False,
        canon_backend: str | None = None,
        solver_opts: dict | None = None,
    ):
        """Returns the problem data used in the call to the solver.

        When a problem is solved, CVXPY creates a chain of reductions enclosed
        in a :class:`~cvxpy.reductions.solvers.solving_chain.SolvingChain`,
        and compiles it to some low-level representation that is
        compatible with the targeted solver. This method returns that low-level
        representation.

        For some solving chains, this low-level representation is a dictionary
        that contains exactly those arguments that were supplied to the solver;
        however, for other solving chains, the data is an intermediate
        representation that is compiled even further by the solver interfaces.

        A solution to the equivalent low-level problem can be obtained via the
        data by invoking the `solve_via_data` method of the returned solving
        chain, a thin wrapper around the code external to CVXPY that further
        processes and solves the problem. Invoke the unpack_results method
        to recover a solution to the original problem.

        For example:

        ::

            objective = ...
            constraints = ...
            problem = cp.Problem(objective, constraints)
            data, chain, inverse_data = problem.get_problem_data(cp.SCS)
            # calls SCS using `data`
            soln = chain.solve_via_data(problem, data)
            # unpacks the solution returned by SCS into `problem`
            problem.unpack_results(soln, chain, inverse_data)

        Alternatively, the `data` dictionary returned by this method
        contains enough information to bypass CVXPY and call the solver
        directly.

        For example:

        ::

            problem = cp.Problem(objective, constraints)
            data, _, _ = problem.get_problem_data(cp.SCS)

            import scs
            probdata = {
              'A': data['A'],
              'b': data['b'],
              'c': data['c'],
            }