Middle-End Reductions
The reductions listed here are not specific to a type of solver.
They can be applied regardless of whether you wish to target, for example,
a quadratic program solver or a conic solver.
Please see our disclaimer
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
class cvxpy.reductions.cvx_attr2constr. CvxAttr2Constr ( problem = None reduce_bounds : bool = False ) [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 ) → Solution [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
reduction_attributes ( ) → list [ str ] [source]
Returns the attributes that will be reduced.
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 : Expression args : list canonicalize_params : bool = True ) [source]
Canonicalize an expression, w.r.t. canonicalized arguments.
Parameters:
expr : Expression Expression to canonicalize.
args : list Arguments to the expression.
canonicalize_params : bool = True Should constant subtrees
containing parameters be canonicalized?
Returns:
canonicalized expression, constraints
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
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
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: 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:
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