DianaOptimization: rosenbrock.py

# ------------------------------------------------------------------
#  Diana process modelling, simulation and analysis software
#  Copyright (c) 2006, Sergiy Gogolenko
#  e-mail: gogolenk@mpi-magdeburg.mpg.de
#  All rights reserved.
# ------------------------------------------------------------------
#  $Id: rosenbrock.py Fri Aug 11 13:38:55 2006 gogolenk $
# ------------------------------------------------------------------
#  Written under: i586-suse-linux 
# ------------------------------------------------------------------
#  Last update: Fri Aug 11 13:38:55 2006
# ------------------------------------------------------------------
#  Description:
#   Simple example of using DIANA in order to find minimum of 
#  Rosenbrocks' function (10 unknowns) with deterministic 
#  optimizer (in this case LBFGS-solver).

__author__ = "Sergiy Gogolenko (gogolenk@mpi-magdeburg.mpg.de)"
__version__ = "@PACKAGE_VERSION@"
__date__ = "$Date: Fri Aug 11 13:38:55 2006 $"
__copyright__ = "Copyright (c) 2006 Sergiy Gogolenko"
__license__ = ""

import diana, sys
from math import *

optimizerName = "lbfgsb" # "direct" 

# initialize Diana main class
dmain = diana.GetDianaMain(sys.argv)

# create LBFGSB-optimizer
sfactory = dmain.GetSolverFactory()
solver = sfactory.CreateSolver(diana.CAPE_NLP, None, optimizerName)

# specify list of sought parameters
sp = []
for i in range(10):
    sp.append(diana.DianaNLPRealParameterSpec
              ("x"+str(i+1), "x"+str(i+1), 2, -512, 512, 0.001))

# create task
task=diana.BasicNLPTask(sp)

# define objective
def objfunc_Rosenbrock(task):
    pars = task.GetNLPParameters()
    par = pars.GetParameterValue(0)
    objfunc = 0.
    for i in xrange(pars.GetParametersCount()-1):
        par_next = pars.GetParameterValue(i+1)
        objfunc += 100*((par_next - par**2)**2) + ((par-1)**2)
        par = par_next
    return objfunc

# assign objective with task
task.SetObjFunction(objfunc_Rosenbrock)
task.Initialize()
print "Rosenbrock task initialized"

# create report
report = dmain.CreateReportingInterface(optimizerName)

# set parameters for reporting
pars_rep = report.GetParameters()
pars_rep["Author"].SetValue("MG")
pars_rep["OutputFolder"].SetValue("./report")
pars_rep["ConsoleOutput"].SetValue(True)

report.Initialize()
print "Report initialized"

# assign prepared task and report to solver
solver.SetReportingInterface(report)
solver.SetNLPTask(task)
solver.Initialize()
print "LBFGSB optimizer initialized"

# start optimization
print "Rosenbrock function (%i parameters) optimization..." \
      % (task.GetSoughtParameters().Count())
try:
    solver.Solve()
except:
    print "Solving error"
pars_opt = solver.GetParameters()
print '#'*80
print "Found minimum:                   ", \
      pars_opt["MinObjFunc"].GetValue()
print "Total amount of objective calls: ", \
      pars_opt["TotalEvalsCount"].GetValue()
print "Total amount of iterations:      ", \
      pars_opt["CurrIteration"].GetValue()
print "Total execution time:             %s sec" % \
      pars_opt["TotalTimeOfWork"].GetValue()
print '#'*80
print "Solution: ", solver.GetSolution()
print '#'*80