feat: 初始化Easy Patch插件及依赖文件
- 添加Blender插件核心文件：__init__.py、ui.py、property.py、preference.py - 添加插件工具模块：g.py、loop.py、generate_loop.py、const.py、op.py - 添加翻译工具：utils/trans.py - 添加PuLP线性规划库及其依赖文件，包括CBC求解器二进制文件 - 添加.gitignore和VSCode配置文件
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345
utils/pulp/apis/mosek_api.py
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345
utils/pulp/apis/mosek_api.py
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# PuLP : Python LP Modeler
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# Version 1.4.2
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# Copyright (c) 2002-2005, Jean-Sebastien Roy (js@jeannot.org)
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# Modifications Copyright (c) 2007- Stuart Anthony Mitchell (s.mitchell@auckland.ac.nz)
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# $Id:solvers.py 1791 2008-04-23 22:54:34Z smit023 $
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the
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# "Software"), to deal in the Software without restriction, including
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# without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to
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# permit persons to whom the Software is furnished to do so, subject to
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# the following conditions:
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
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# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
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# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
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# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."""
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from .core import LpSolver, PulpSolverError
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from .. import constants
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import sys
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from typing import Optional
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class MOSEK(LpSolver):
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"""Mosek lp and mip solver (via Mosek Optimizer API)."""
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name = "MOSEK"
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try:
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global mosek
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import mosek
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env = mosek.Env()
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except ImportError:
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def available(self):
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"""True if Mosek is available."""
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return False
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def actualSolve(self, lp, callback=None):
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"""Solves a well-formulated lp problem."""
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raise PulpSolverError("MOSEK : Not Available")
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else:
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def __init__(
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self,
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mip=True,
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msg=True,
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timeLimit: Optional[float] = None,
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options: Optional[dict] = None,
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task_file_name="",
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sol_type=mosek.soltype.bas,
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):
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"""Initializes the Mosek solver.
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Keyword arguments:
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@param mip: If False, then solve MIP as LP.
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@param msg: Enable Mosek log output.
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@param float timeLimit: maximum time for solver (in seconds)
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@param options: Accepts a dictionary of Mosek solver parameters. Ignore to
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use default parameter values. Eg: options = {mosek.dparam.mio_max_time:30}
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sets the maximum time spent by the Mixed Integer optimizer to 30 seconds.
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Equivalently, one could also write: options = {"MSK_DPAR_MIO_MAX_TIME":30}
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which uses the generic parameter name as used within the solver, instead of
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using an object from the Mosek Optimizer API (Python), as before.
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@param task_file_name: Writes a Mosek task file of the given name. By default,
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no task file will be written. Eg: task_file_name = "eg1.opf".
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@param sol_type: Mosek supports three types of solutions: mosek.soltype.bas
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(Basic solution, default), mosek.soltype.itr (Interior-point
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solution) and mosek.soltype.itg (Integer solution).
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For a full list of Mosek parameters (for the Mosek Optimizer API) and supported task file
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formats, please see https://docs.mosek.com/9.1/pythonapi/parameters.html#doc-all-parameter-list.
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"""
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self.mip = mip
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self.msg = msg
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self.timeLimit = timeLimit
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self.task_file_name = task_file_name
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self.solution_type = sol_type
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if options is None:
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options = {}
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self.options = options
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if self.timeLimit is not None:
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timeLimit_keys = {"MSK_DPAR_MIO_MAX_TIME", mosek.dparam.mio_max_time}
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if not timeLimit_keys.isdisjoint(self.options.keys()):
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raise ValueError(
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"timeLimit parameter has been provided trough `timeLimit` and `options`."
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)
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self.options["MSK_DPAR_MIO_MAX_TIME"] = self.timeLimit
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def available(self):
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"""True if Mosek is available."""
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return True
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def setOutStream(self, text):
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"""Sets the log-output stream."""
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sys.stdout.write(text)
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sys.stdout.flush()
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def buildSolverModel(self, lp, inf=1e20):
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"""Translate the problem into a Mosek task object."""
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self.cons = lp.constraints
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self.numcons = len(self.cons)
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self.cons_dict = {}
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i = 0
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for c in self.cons:
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self.cons_dict[c] = i
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i = i + 1
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self.vars = list(lp.variables())
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self.numvars = len(self.vars)
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self.var_dict = {}
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# Checking for repeated names
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lp.checkDuplicateVars()
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self.task = MOSEK.env.Task()
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self.task.appendcons(self.numcons)
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self.task.appendvars(self.numvars)
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if self.msg:
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self.task.set_Stream(mosek.streamtype.log, self.setOutStream)
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# Adding variables
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for i in range(self.numvars):
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vname = self.vars[i].name
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self.var_dict[vname] = i
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self.task.putvarname(i, vname)
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# Variable type (Default: Continuous)
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if self.mip & (self.vars[i].cat == constants.LpInteger):
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self.task.putvartype(i, mosek.variabletype.type_int)
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self.solution_type = mosek.soltype.itg
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# Variable bounds
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vbkey = mosek.boundkey.fr
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vup = inf
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vlow = -inf
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if self.vars[i].lowBound != None:
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vlow = self.vars[i].lowBound
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if self.vars[i].upBound != None:
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vup = self.vars[i].upBound
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vbkey = mosek.boundkey.ra
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else:
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vbkey = mosek.boundkey.lo
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elif self.vars[i].upBound != None:
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vup = self.vars[i].upBound
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vbkey = mosek.boundkey.up
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self.task.putvarbound(i, vbkey, vlow, vup)
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# Objective coefficient for the current variable.
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self.task.putcj(i, lp.objective.get(self.vars[i], 0.0))
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# Coefficient matrix
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self.A_rows, self.A_cols, self.A_vals = zip(
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*[
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[self.cons_dict[row], self.var_dict[col], coeff]
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for col, row, coeff in lp.coefficients()
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]
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)
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self.task.putaijlist(self.A_rows, self.A_cols, self.A_vals)
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# Constraints
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self.constraint_data_list = []
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for c in self.cons:
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cname = self.cons[c].name
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if cname != None:
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self.task.putconname(self.cons_dict[c], cname)
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else:
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self.task.putconname(self.cons_dict[c], c)
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csense = self.cons[c].sense
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cconst = -self.cons[c].constant
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clow = -inf
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cup = inf
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# Constraint bounds
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if csense == constants.LpConstraintEQ:
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cbkey = mosek.boundkey.fx
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clow = cconst
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cup = cconst
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elif csense == constants.LpConstraintGE:
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cbkey = mosek.boundkey.lo
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clow = cconst
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elif csense == constants.LpConstraintLE:
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cbkey = mosek.boundkey.up
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cup = cconst
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else:
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raise PulpSolverError("Invalid constraint type.")
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self.constraint_data_list.append([self.cons_dict[c], cbkey, clow, cup])
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self.cons_id_list, self.cbkey_list, self.clow_list, self.cup_list = zip(
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*self.constraint_data_list
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)
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self.task.putconboundlist(
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self.cons_id_list, self.cbkey_list, self.clow_list, self.cup_list
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)
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# Objective sense
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if lp.sense == constants.LpMaximize:
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self.task.putobjsense(mosek.objsense.maximize)
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else:
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self.task.putobjsense(mosek.objsense.minimize)
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def findSolutionValues(self, lp):
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"""
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Read the solution values and status from the Mosek task object. Note: Since the status
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map from mosek.solsta to LpStatus is not exact, it is recommended that one enables the
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log output and then refer to Mosek documentation for a better understanding of the
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solution (especially in the case of mip problems).
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"""
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self.solsta = self.task.getsolsta(self.solution_type)
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self.solution_status_dict = {
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mosek.solsta.optimal: constants.LpStatusOptimal,
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mosek.solsta.prim_infeas_cer: constants.LpStatusInfeasible,
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mosek.solsta.dual_infeas_cer: constants.LpStatusUnbounded,
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mosek.solsta.unknown: constants.LpStatusUndefined,
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mosek.solsta.integer_optimal: constants.LpStatusOptimal,
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mosek.solsta.prim_illposed_cer: constants.LpStatusNotSolved,
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mosek.solsta.dual_illposed_cer: constants.LpStatusNotSolved,
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mosek.solsta.prim_feas: constants.LpStatusNotSolved,
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mosek.solsta.dual_feas: constants.LpStatusNotSolved,
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mosek.solsta.prim_and_dual_feas: constants.LpStatusNotSolved,
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}
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# Variable values.
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try:
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self.xx = [0.0] * self.numvars
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self.task.getxx(self.solution_type, self.xx)
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for var in lp.variables():
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var.varValue = self.xx[self.var_dict[var.name]]
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except mosek.Error:
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pass
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# Constraint slack variables.
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try:
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self.xc = [0.0] * self.numcons
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self.task.getxc(self.solution_type, self.xc)
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for con in lp.constraints:
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lp.constraints[con].slack = -(
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self.cons[con].constant + self.xc[self.cons_dict[con]]
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)
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except mosek.Error:
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pass
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# Reduced costs.
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if self.solution_type != mosek.soltype.itg:
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try:
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self.x_rc = [0.0] * self.numvars
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self.task.getreducedcosts(
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self.solution_type, 0, self.numvars, self.x_rc
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)
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for var in lp.variables():
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var.dj = self.x_rc[self.var_dict[var.name]]
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except mosek.Error:
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pass
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# Constraint Pi variables.
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try:
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self.y = [0.0] * self.numcons
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self.task.gety(self.solution_type, self.y)
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for con in lp.constraints:
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lp.constraints[con].pi = self.y[self.cons_dict[con]]
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except mosek.Error:
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pass
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def putparam(self, par, val):
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"""
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Pass the values of valid parameters to Mosek.
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"""
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if isinstance(par, mosek.dparam):
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self.task.putdouparam(par, val)
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elif isinstance(par, mosek.iparam):
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self.task.putintparam(par, val)
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elif isinstance(par, mosek.sparam):
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self.task.putstrparam(par, val)
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elif isinstance(par, str):
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if par.startswith("MSK_DPAR_"):
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self.task.putnadouparam(par, val)
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elif par.startswith("MSK_IPAR_"):
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self.task.putnaintparam(par, val)
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elif par.startswith("MSK_SPAR_"):
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self.task.putnastrparam(par, val)
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else:
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raise PulpSolverError(
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"Invalid MOSEK parameter: '{}'. Check MOSEK documentation for a list of valid parameters.".format(
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par
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)
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)
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def actualSolve(self, lp):
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"""
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Solve a well-formulated lp problem.
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"""
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self.buildSolverModel(lp)
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# Set solver parameters
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for msk_par in self.options:
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self.putparam(msk_par, self.options[msk_par])
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# Task file
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if self.task_file_name:
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self.task.writedata(self.task_file_name)
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# Optimize
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self.task.optimize()
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# Mosek solver log (default: standard output stream)
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if self.msg:
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self.task.solutionsummary(mosek.streamtype.msg)
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self.findSolutionValues(lp)
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lp.assignStatus(self.solution_status_dict[self.solsta])
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for var in lp.variables():
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var.modified = False
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for con in lp.constraints.values():
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con.modified = False
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return lp.status
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def actualResolve(self, lp, inf=1e20, **kwargs):
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"""
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Modify constraints and re-solve an lp. The Mosek task object created in the first solve is used.
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"""
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for c in self.cons:
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if self.cons[c].modified:
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csense = self.cons[c].sense
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cconst = -self.cons[c].constant
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clow = -inf
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cup = inf
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# Constraint bounds
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if csense == constants.LpConstraintEQ:
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cbkey = mosek.boundkey.fx
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clow = cconst
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cup = cconst
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elif csense == constants.LpConstraintGE:
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cbkey = mosek.boundkey.lo
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clow = cconst
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elif csense == constants.LpConstraintLE:
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cbkey = mosek.boundkey.up
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cup = cconst
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else:
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raise PulpSolverError("Invalid constraint type.")
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self.task.putconbound(self.cons_dict[c], cbkey, clow, cup)
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# Re-solve
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self.task.optimize()
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self.findSolutionValues(lp)
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lp.assignStatus(self.solution_status_dict[self.solsta])
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for var in lp.variables():
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var.modified = False
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for con in lp.constraints.values():
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con.modified = False
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return lp.status
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