#!/usr/bin/env python # -*- coding: utf-8 -*- """ *********************************************************************************** opt_tutorial1.py DAE Tools: pyDAE module, www.daetools.com Copyright (C) Dragan Nikolic *********************************************************************************** DAE Tools is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation. DAE Tools is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with the DAE Tools software; if not, see <http://www.gnu.org/licenses/>. ************************************************************************************ """ __doc__ = """ This tutorial introduces IPOPT NLP solver, its setup and options. """ import sys from time import localtime, strftime from daetools.pyDAE import * from daetools.solvers.ipopt import pyIPOPTclassmodTutorial(daeModel):def__init__(self, Name, Parent = None, Description = ""): daeModel.__init__(self, Name, Parent, Description) self.x1 = daeVariable("x1", no_t, self) self.x2 = daeVariable("x2", no_t, self) self.x3 = daeVariable("x3", no_t, self) self.x4 = daeVariable("x4", no_t, self) self.dummy = daeVariable("dummy", no_t, self, "A dummy variable to satisfy the condition that there should be at least one-state variable and one equation in a model")defDeclareEquations(self): daeModel.DeclareEquations(self) eq = self.CreateEquation("Dummy") eq.Residual = self.dummy()classsimTutorial(daeSimulation):def__init__(self): daeSimulation.__init__(self) self.m = modTutorial("opt_tutorial1") self.m.Description = __doc__defSetUpParametersAndDomains(self):passdefSetUpVariables(self): self.m.x1.AssignValue(1) self.m.x2.AssignValue(5) self.m.x3.AssignValue(5) self.m.x4.AssignValue(1)defSetUpOptimization(self): # Set the objective function (minimization). # The objective function can be accessed by using ObjectiveFunction property which always returns the 1st obj. function, # for in general case more than one obj. function. can be defined, so ObjectiveFunctions[0] can be used as well: # self.ObjectiveFunctions[0].Residual = ... # Obviously defining more than one obj. function has no meaning when using opt. software such as Ipopt, Bonmin or Nlopt # which cannot do the multi-objective optimization. The number of obj. functions can be defined in the function # optimization.Initialize by using the named argument NumberOfObjectiveFunctions (the default is 1). # Other obj. functions can be obtained by using ObjectiveFunctions[i] property. self.ObjectiveFunction.Residual = self.m.x1() * self.m.x4() * (self.m.x1() + self.m.x2() + self.m.x3()) + self.m.x3() # Set the constraints (inequality, equality) # Constraints are in the following form: # - Inequality: g(i) <= 0 # - Equality: h(i) = 0 self.c1 = self.CreateInequalityConstraint("Constraint 1") # g(x) >= 25: 25 - x1*x2*x3*x4 <= 0 self.c1.Residual = 25 - self.m.x1() * self.m.x2() * self.m.x3() * self.m.x4() self.c2 = self.CreateEqualityConstraint("Constraint 2") # h(x) == 40 self.c2.Residual = self.m.x1() * self.m.x1() + self.m.x2() * self.m.x2() + self.m.x3() * self.m.x3() + self.m.x4() * self.m.x4() - 40 # Set the optimization variables, their lower/upper bounds and the starting point # The optimization variables can be stored and used later to get the optimization results or # to interact with some 3rd party software. self.x1 = self.SetContinuousOptimizationVariable(self.m.x1, 1, 5, 2); self.x2 = self.SetContinuousOptimizationVariable(self.m.x2, 1, 5, 2); self.x3 = self.SetContinuousOptimizationVariable(self.m.x3, 1, 5, 2); self.x4 = self.SetContinuousOptimizationVariable(self.m.x4, 1, 5, 2);defsetOptions(nlpsolver): # 1) Set the options manuallytry: nlpsolver.SetOption('print_level', 0) nlpsolver.SetOption('tol', 1e-7) nlpsolver.SetOption('mu_strategy', 'adaptive') # Print options loaded at pyIPOPT startup and the user set options: #nlpsolver.PrintOptions() #nlpsolver.PrintUserOptions() # ClearOptions can clear all options: #nlpsolver.ClearOptions()exceptException as e:str(e)) # Use daeSimulator classdefguiRun(app): sim = simTutorial() opt = daeOptimization() nlp = pyIPOPT.daeIPOPT() sim.m.SetReportingOn(True) sim.ReportingInterval = 1 sim.TimeHorizon = 5 simulator = daeSimulator(app, simulation = sim, optimization = opt, nlpsolver = nlp, nlpsolver_setoptions_fn = setOptions) simulator.exec_() # Setup everything manually and run in a consoledefconsoleRun(): # Create Log, Solver, DataReporter and Simulation object log = daePythonStdOutLog() daesolver = daeIDAS() nlpsolver = pyIPOPT.daeIPOPT() datareporter = daeTCPIPDataReporter() simulation = simTutorial() optimization = daeOptimization() # Do no print progress log.PrintProgress = False # Enable reporting of all variables simulation.m.SetReportingOn(True) # Set the time horizon and the reporting interval simulation.ReportingInterval = 1 simulation.TimeHorizon = 5 # Connect data reporter simName = simulation.m.Name + strftime(" [%d.%m.%Y %H:%M:%S]", localtime())if(datareporter.Connect("", simName) == False): sys.exit() # Initialize the optimization optimization.Initialize(simulation, nlpsolver, daesolver, datareporter, log) # Achtung! Achtung! NLP solver options can only be set after optimization.Initialize() # Otherwise seg. fault occurs for some reasons. setOptions(nlpsolver) # Save the model report and the runtime model report simulation.m.SaveModelReport(simulation.m.Name + ".xml") simulation.m.SaveRuntimeModelReport(simulation.m.Name + "-rt.xml") # Run optimization.Run() optimization.Finalize()if__name__ == "__main__":iflen(sys.argv) > 1and(sys.argv[1] == 'console'): consoleRun()else: app = daeCreateQtApplication(sys.argv) guiRun(app)