bi* dZddlmZmZmZddlmZddlm Z ddl m Z m Z ddlmZddlmZddlmZddlmZmZdd lmZ dd ed eeed eeed dfdZGddeZy)a, Copyright 2013 Steven Diamond Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. )ListOptionalTupleN)sumtrace)Constant) Expression)Variable)MaximizeMinimize)Problemprobopt_vars dont_opt_varsreturnPartialProblemc @| | td|D|Dcgc] }t|}}|jDcgc]}t||vs|}}n|D|Dcgc] }t|}}|jDcgc]}t||vs|}}nL|J|H||zDcgc] }t|}}|jD]}t||vstd|Dcic],}t|t|jfi|j .}}|j j|}|jD cgc]} | j|} } t|| } t| |||fi|Scc}wcc}wcc}wcc}wcc}wcc}wcc} w)awPartially optimizes the given problem over the specified variables. Either opt_vars or dont_opt_vars must be given. If both are given, they must contain all the variables in the problem. Partial optimize is useful for two-stage optimization and graph implementations. For example, we can write .. code :: python x = Variable(n) t = Variable(n) abs_x = partial_optimize(Problem(Minimize(sum(t)), [-t <= x, x <= t]), opt_vars=[t]) to define the entrywise absolute value of x. Parameters ---------- prob : Problem The problem to partially optimize. opt_vars : list, optional The variables to optimize over. dont_opt_vars : list, optional The variables to not optimize over. solver : str, optional The default solver to use for value and grad. kwargs : keywords, optional Additional solver specific keyword arguments. Returns ------- Expression An expression representing the partial optimization. Convex for minimization objectives and concave for maximization objectives. z@partial_optimize called with neither opt_vars nor dont_opt_vars.z`If opt_vars and new_opt_vars are both specified, they must contain all variables in the problem.) ValueErrorid variablesr shape attributes objective tree_copy constraintsr r) rrrsolverkwargsvarids id_to_new_varnew_objcon new_constrs new_var_probs \/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/cvxpy/transforms/partial_optimize.pypartial_optimizer&sXM1 N    "/03r#w00#'>>#3JCr#wc7ICJJ  "*+3r#w++(,(8OBsG3># C#wc! G EMN=@Whsyy9),99NMNnn&&}5G"..0==/0K07K0L ,- R6 RR-1J,O;N0s4E=FFFF F 'F,1FFcPeZdZdZdedeedeeddffd ZdZde fd Z de fd Z de fd Z dd e de fd Zde fdZde fdZde fdZde fdZde fdZde fdZedeedffdZde fdZdeefdZdZdeefdZedZedZedZ dZ!xZ"S)rzA partial optimization problem. Attributes ---------- opt_vars : list The variables to optimize over. dont_opt_vars : list The variables to not optimize over. rrrrNc r||_||_||_|g|_||_t t |y)N)rrrargs _solve_kwargssuperr__init__)selfrrrrr __class__s r%r,zPartialProblem.__init__ss9! * F # nd,.cH|j|j|jgS)zLReturns info needed to reconstruct the expression besides the args. )rrrr-s r%get_datazPartialProblem.get_data}s t114;;??r/cTt|jdjdk(S)Nr)lenr)rr1s r% is_constantzPartialProblem.is_constants#499Q<))+,11r/c|jdjxr)t|jdjtk(Sz"Is the expression convex? r)r)is_dcptyperr r1s r% is_convexzPartialProblem.is_convex=yy|""$5 1'' (H 4 5r/c|jdjxr)t|jdjtk(S)z#Is the expression concave? r)r)r8r9rr r1s r% is_concavezPartialProblem.is_concaver;r/contextcz|jdvr|jdj|Std|)zBThe expression is a disciplined parameterized expression. )dcpdgprzUnsupported context)lowerr)is_dppr)r-r>s r%rCzPartialProblem.is_dpps9 ==?n ,99Q<&&w/ /2G< >#doo&77D99Q<))..q1D))A,22 ("&"4"4TF>#CI%DE$K'D (yyH484F4FGSc8C=(GFGVV&&t,F>># )C (CI ) =C0Hs I3 I8c|jdjjd}|jdj|jzS)znA list of constraints describing the closure of the region where the expression is finite. r)r)rrdomain)r-obj_exprs r%rxzPartialProblem.domain s? 99Q<))..q1yy|''(//99r/c |jDcic]}|j|j}}g}|jD]#}|jy|||jk(gz }%t |j dj ||j djz}|jdd|ji|j|jD]}||j|_|jjScc}w)zoReturns the numeric value of the expression. Returns: A numpy matrix or a scalar. NrrrO) rrrcrr r)rrrerr* _solutionopt_val)r-rrnrors r%rczPartialProblem.values261AB#CFFCII%BB%% /Cyy SCII-..  / tyy|--x$))A,:R:R/RS <$++<););<>># )C (CI )~~%%%CsD c|jdjjdj\}}|jdjD]}||jdz }||fS)zReturns the graph implementation of the object. Change the ids of all the opt_vars. Returns ------- A tuple of (affine expression, [constraints]). r)r)rcanonical_formr)r-objconstrsconss r% canonicalizezPartialProblem.canonicalize)shyy|--2215DD WIIaL,, .D t**1- -G .W~r/)r@)#__name__ __module__ __qualname____doc__r rr r,r2boolr5r:r=strrCrFrHrJrMrPrSpropertyrintrrXrr\rr^rarxrcr __classcell__)r.s@r%rrhsP//+/>/>/@D/@ 2T2545 5D5 =c=d=545 5D5 :4: :4:  D uS#X 1c1 (4>( ) (4>( 33j::&&,r/)NNN)rtypingrrrcvxpy.settingssettingsrgcvxpy.utilities utilitiesr` cvxpy.atomsrr$cvxpy.expressions.constants.constantrcvxpy.expressions.expressionr cvxpy.expressions.variabler cvxpy.problems.objectiver r cvxpy.problems.problemr r&rrOr/r%rs )("93/7* *..2  HS HStH~&HSDN+HS  HSVOZOr/