bi dZddlmZmZddlmZddlmZmZddl m Z deeeefdeeeffdZ ddeeeefd e deeeffd Z deeeefdefd Z ddeeeefd e defd Zy)a, Copyright 2017 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. )ListUnionN)MaximizeMinimize) indicator objectivesreturncXt}tfdt|DS)zCombines objectives as a weighted sum. Args: objectives: A list of Minimize/Maximize objectives. weights: A vector of weights. Returns: A Minimize/Maximize objective. c34K|]}||zyw)N).0irweightss U/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/cvxpy/transforms/scalarize.py zweighted_sum.."sAAz!}WQZ'As)lensumrange)rrnum_objss`` r weighted_sumrs#:H AxA AA off_targetc `t|t|k(sJdt|t|k(sJd|t|t|k(sJd|dk\sJdt|}g}t|D]}|||||||nd} } }||dkr | | | | nd} } }|jdjrdnd} | |jd||z z} | t |||z zt j | z} | ||jdzz } |+| | t| |jdz| ||zkgzz } |j| t|}|jr t|S|jr t|Std ) a Combines objectives with penalties within a range between target and limit. For nonnegative priorities, each Minimize objective i has value off_target*objectives[i] when objectives[i] < targets[i] (priorities[i]-off_target)*objectives[i] when targets[i] <= objectives[i] <= limits[i] +infinity when objectives[i] > limits[i] and each Maximize objective i has value off_target*objectives[i] when objectives[i] > targets[i] (priorities[i]-off_target)*objectives[i] when targets[i] >= objectives[i] >= limits[i] -infinity when objectives[i] < limits[i] A negative priority flips the objective sense, i.e., we use -objectives[i], -targets[i], and -limits[i] with abs(priorities[i]). Args: objectives: A list of Minimize/Maximize objectives. priorities: The weight within the trange. targets: The start (end) of penalty for Minimize (Maximize) limits: Optional hard end (start) of penalty for Minimize (Maximize) off_target: Penalty outside of target. Returns: A Minimize/Maximize objective. Raises: ValueError: If the scalarized objective is neither convex nor concave. z/Number of objectives and priorities must match.z,Number of objectives and targets must match.Nz+Number of objectives and limits must match.rz,The off_target argument must be nonnegative.z3Scalarized objective is neither convex nor concave.)rrargs is_convexabsatomsposrappendrr is_concaver ValueError)r prioritiestargetslimitsrrnew_objsrobjtarlimsigndeltaexprobj_exprs rtargets_and_prioritiesr/%sT z?c*o -`/`` - z?c'l *Z,ZZ * :#f+-\/\\- ?JJJ?:H02H 8_ "1 wqz@R6!9X\#S a=1  D3$TcCHHQK))+qchhqkGAJ./SA'*45eii6FF 388A;&&   DD!$4VAY$F#GHH HD 8}H!!    !!NOOrct|}tjt|Dcgc]}||||zjdc}}t |Scc}w)zCombines objectives as max of weighted terms. Args: objectives: A list of Minimize/Maximize objectives. weights: A vector of weights. Returns: A Minimize objective. r)rrmaximumrrr)rrrrr-s rmaxr2msR:H ==uXW!JqM'!*4::1=W XD D>XsAgammact|}t|Dcgc]}||||zjd}}tj|tj |z|z }t |Scc}w)aCombines objectives as log_sum_exp of weighted terms. The objective takes the form log(sum_{i=1}^n exp(gamma*weights[i]*objectives[i]))/gamma As gamma goes to 0, log_sum_exp approaches weighted_sum. As gamma goes to infinity, log_sum_exp approaches max. Args: objectives: A list of Minimize/Maximize objectives. weights: A vector of weights. gamma: Parameter interpolating between weighted_sum and max. Returns: A Minimize objective. r)rrrr log_sum_expvstackr)rrr3rrtermsr-s rr5r5|so&:H9>x IAjmGAJ& , ,Q / IE I   U5<<#66 7 =D D> JsA3)Ngh㈵>)g?)__doc__typingrr cvxpy.atomsrcvxpy.problems.objectiverrcvxpy.transformsrrfloatr/r2r5r rrr>s7& BT%((:";< B%PXZbPbJc B$  FP58+,-FP  FP  8X  FPP Dx123   HK58+,-?D r