Middle-End Reductions

The reductions listed here are not specific to a back end (solver); they can be applied regardless of whether you wish to target, for example, a quadratic program solver or a conic solver.


class cvxpy.reductions.complex2real.complex2real.Complex2Real(problem=None)[source]

Bases: cvxpy.reductions.reduction.Reduction

Lifts complex numbers to a real representation.


class cvxpy.reductions.cvx_attr2constr.CvxAttr2Constr(problem=None)[source]

Bases: cvxpy.reductions.reduction.Reduction

Expand convex variable attributes into constraints.


class cvxpy.reductions.dgp2dcp.dgp2dcp.Dgp2Dcp(problem=None)[source]

Bases: cvxpy.reductions.canonicalization.Canonicalization

Reduce DGP problems to DCP problems.

This reduction takes as input a DGP problem and returns an equivalent DCP problem. Because every (generalized) geometric program is a DGP problem, this reduction can be used to convert geometric programs into convex form.


>>> import cvxpy as cp
>>> x1 = cp.Variable(pos=True)
>>> x2 = cp.Variable(pos=True)
>>> x3 = cp.Variable(pos=True)
>>> monomial = 3.0 * x_1**0.4 * x_2 ** 0.2 * x_3 ** -1.4
>>> posynomial = monomial + 2.0 * x_1 * x_2
>>> dgp_problem = cp.Problem(cp.Minimize(posynomial), [monomial == 4.0])
>>> dcp2cone = cvxpy.reductions.Dcp2Cone()
>>> assert not dcp2cone.accepts(dgp_problem)
>>> gp2dcp = cvxpy.reductions.Dgp2Dcp(dgp_problem)
>>> dcp_problem = gp2dcp.reduce()
>>> assert dcp2cone.accepts(dcp_problem)
>>> dcp_probem.solve()
>>> dgp_problem.unpack(gp2dcp.retrieve(dcp_problem.solution))
>>> print(dgp_problem.value)
>>> print(dgp_problem.variables())

A problem is accepted if it is DGP.


Converts a DGP problem to a DCP problem.


class cvxpy.reductions.eval_params.EvalParams(problem=None)[source]

Bases: cvxpy.reductions.reduction.Reduction

Replaces symbolic parameters with their constant values.


Replace parameters with constant values.

Parameters:problem (Problem) – The problem whose parameters should be evaluated.
Returns:A new problem where the parameters have been converted to constants.
Return type:Problem
Raises:ParameterError – If the problem has unspecified parameters (i.e., a parameter whose value is None).
invert(solution, inverse_data)[source]

Returns a solution to the original problem given the inverse_data.


class cvxpy.reductions.flip_objective.FlipObjective(problem=None)[source]

Bases: cvxpy.reductions.reduction.Reduction

Flip a minimization objective to a maximization and vice versa.


\(\max(f(x)) = -\min(-f(x))\)

Parameters:problem (Problem) – The problem whose objective is to be flipped.
  • Problem – A problem with a flipped objective.
  • list – The inverse data.
invert(solution, inverse_data)[source]

Map the solution of the flipped problem to that of the original.

  • solution (Solution) – A solution object.
  • inverse_data (list) – The inverse data returned by an invocation to apply.

A solution to the original problem.

Return type: