中间-末端的缩减

这里列出的缩减不特定于求解器的类型。 无论您想要目标是二次规划求解器还是锥求解器,都可以应用它们。

在直接在您的代码中使用这些之前,请参阅 我们的免责声明 关于减少API使用。

复数转实数

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

Bases: Reduction

Lifts complex numbers to a real representation.

accepts(problem) None[source]

States whether the reduction accepts a problem.

Parameters:

problem (Problem) – The problem to check.

Returns:

True if the reduction can be applied, False otherwise.

Return type:

bool

apply(problem)[source]

Applies the reduction to a problem and returns an equivalent problem.

Parameters:

problem (Problem) – The problem to which the reduction will be applied.

Returns:

  • Problem or dict – An equivalent problem, encoded either as a Problem or a dict.

  • InverseData, list or dict – Data needed by the reduction in order to invert this particular application.

invert(solution, inverse_data)[source]

Returns a solution to the original problem given the inverse_data.

Parameters:
  • solution (Solution) – A solution to a problem that generated the inverse_data.

  • inverse_data – The data encoding the original problem.

Returns:

A solution to the original problem.

Return type:

Solution

CvxAttr转化为约束

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

Bases: Reduction

Expand convex variable attributes into constraints.

accepts(problem) bool[source]

States whether the reduction accepts a problem.

Parameters:

problem (Problem) – The problem to check.

Returns:

True if the reduction can be applied, False otherwise.

Return type:

bool

apply(problem)[source]

Applies the reduction to a problem and returns an equivalent problem.

Parameters:

problem (Problem) – The problem to which the reduction will be applied.

Returns:

  • Problem or dict – An equivalent problem, encoded either as a Problem or a dict.

  • InverseData, list or dict – Data needed by the reduction in order to invert this particular application.

invert(solution, inverse_data)[source]

Returns a solution to the original problem given the inverse_data.

Parameters:
  • solution (Solution) – A solution to a problem that generated the inverse_data.

  • inverse_data – The data encoding the original problem.

Returns:

A solution to the original problem.

Return type:

Solution

Dgp转化为Dcp

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

Bases: 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.

Example

>>> 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_problem.solve()
>>>
>>> dgp_problem.unpack(gp2dcp.retrieve(dcp_problem.solution))
>>> print(dgp_problem.value)
>>> print(dgp_problem.variables())
accepts(problem)[source]

A problem is accepted if it is DGP.

apply(problem)[source]

Converts a DGP problem to a DCP problem.

canonicalize_expr(expr, args)[source]

Canonicalize an expression, w.r.t. canonicalized arguments.

invert(solution, inverse_data)[source]

Returns a solution to the original problem given the inverse_data.

Parameters:
  • solution (Solution) – A solution to a problem that generated the inverse_data.

  • inverse_data – The data encoding the original problem.

Returns:

A solution to the original problem.

Return type:

Solution

评估参数

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

Bases: Reduction

Replaces symbolic parameters with their constant values.

accepts(problem) bool[source]

States whether the reduction accepts a problem.

Parameters:

problem (Problem) – The problem to check.

Returns:

True if the reduction can be applied, False otherwise.

Return type:

bool

apply(problem)[source]

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.

FlipObjective

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

Bases: Reduction

Flip a minimization objective to a maximization and vice versa.

accepts(problem) bool[source]

States whether the reduction accepts a problem.

Parameters:

problem (Problem) – The problem to check.

Returns:

True if the reduction can be applied, False otherwise.

Return type:

bool

apply(problem)[source]

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

Parameters:

problem (Problem) – The problem whose objective is to be flipped.

Returns:

  • 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.

Parameters:
  • solution (Solution) – A solution object.

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

Returns:

A solution to the original problem.

Return type:

Solution