QuickStart Samples

# Optimization In Multiple Dimensions QuickStart Sample (IronPython)

Illustrates the use of the multi-dimensional optimizer classes in the Extreme.Mathematics.Optimization namespace for optimization in multiple dimensions in IronPython.

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import numerics # The optimization classes reside in the # Extreme.Mathematics.Optimization namespace. from Extreme.Mathematics.Optimization import * # Function delegates reside in the Extreme.Mathematics # namespace. from Extreme.Mathematics import * # Vectors reside in the Extreme.Mathematics.LinearAlgebra # namespace. from Extreme.Mathematics.LinearAlgebra import * #/ Illustrates unconstrained optimization in multiple dimensions #/ using classes in the Extreme.Mathematics.Optimization #/ namespace of the Extreme Optimization Numerical Libraries #/ for .NET. # # Objective function # # The objective function must be supplied as a # Func<Vector, double> delegate. This is a method # that takes one Vector argument and returns a real number. # See the end of this sample for definitions of the methods # that are referenced here. # The famous Rosenbrock test function. def fRosenbrock(x): p = (1-x[0]) q = x[1] - x[0]*x[0] return p*p + 105 * q*q # Gradient of the Rosenbrock test function. def gRosenbrock(x, f): # Always assume that the second argument may be null: if f == None: f = Vector.Create(2) p = (1-x[0]) q = x[1] - x[0]*x[0] f[0] = -2*p - 420*x[0]*q f[1] = 210*q return f # The gradient of the objective function can be supplied either # as a MultivariateVectorFunction delegate, or a # MultivariateVectorFunction delegate. The former takes # one vector argument and returns a vector. The latter # takes a second vector argument, which is an existing # vector that is used to return the result. # The initial values are supplied as a vector: initialGuess = Vector.Create(-1.2, 1) # The actual solution is [1, 1]. # # Quasi-Newton methods: BFGS and DFP # # For most purposes, the quasi-Newton methods give # excellent results. There are two variations: DFP and # BFGS. The latter gives slightly better results. # Which method is used, is specified by a constructor # parameter of type QuasiNewtonMethod: bfgs = QuasiNewtonOptimizer(QuasiNewtonMethod.Bfgs) bfgs.InitialGuess = initialGuess bfgs.ExtremumType = ExtremumType.Minimum # Set the ObjectiveFunction: bfgs.ObjectiveFunction = fRosenbrock # Set either the GradientFunction or FastGradientFunction: bfgs.FastGradientFunction = gRosenbrock # The FindExtremum method does all the hard work: bfgs.FindExtremum() print "BFGS Method:" print " Solution:", bfgs.Extremum print " Estimated error:", bfgs.EstimatedError print " # iterations:", bfgs.IterationsNeeded # Optimizers return the number of function evaluations # and the number of gradient evaluations needed: print " # function evaluations:", bfgs.EvaluationsNeeded print " # gradient evaluations:", bfgs.GradientEvaluationsNeeded # # Conjugate Gradient methods # # Conjugate gradient methods exist in three variants: # Fletcher-Reeves, Polak-Ribiere, and positive Polak-Ribiere. # Which method is used, is specified by a constructor # parameter of type ConjugateGradientMethod: cg = ConjugateGradientOptimizer(ConjugateGradientMethod.PositivePolakRibiere) # Everything else works as before: cg.ObjectiveFunction = fRosenbrock cg.FastGradientFunction = gRosenbrock cg.InitialGuess = initialGuess cg.FindExtremum() print "Conjugate Gradient Method:" print " Solution:", cg.Extremum print " Estimated error:", cg.EstimatedError print " # iterations:", cg.IterationsNeeded print " # function evaluations:", cg.EvaluationsNeeded print " # gradient evaluations:", cg.GradientEvaluationsNeeded # # Powell's method # # Powell's method is a conjugate gradient method that # does not require the derivative of the objective function. # It is implemented by the PowellOptimizer class: pw = PowellOptimizer() pw.InitialGuess = initialGuess # Powell's method does not use derivatives: pw.ObjectiveFunction = fRosenbrock pw.FindExtremum() print "Powell's Method:" print " Solution:", pw.Extremum print " Estimated error:", pw.EstimatedError print " # iterations:", pw.IterationsNeeded print " # function evaluations:", pw.EvaluationsNeeded print " # gradient evaluations:", pw.GradientEvaluationsNeeded # # Nelder-Mead method # # Also called the downhill simplex method, the method of Nelder # and Mead is useful for functions that are not tractable # by other methods. For example, other methods # may fail if the objective function is not continuous. # Otherwise it is much slower than other methods. # The method is implemented by the NelderMeadOptimizer class: nm = NelderMeadOptimizer() # The class has three special properties, that help determine # the progress of the algorithm. These parameters have # default values and need not be set explicitly. nm.ContractionFactor = 0.5 nm.ExpansionFactor = 2 nm.ReflectionFactor = -2 # Everything else is the same. nm.SolutionTest.AbsoluteTolerance = 1e-15 nm.InitialGuess = initialGuess # The method does not use derivatives: nm.ObjectiveFunction = fRosenbrock nm.FindExtremum() print "Nelder-Mead Method:" print " Solution:", nm.Extremum print " Estimated error:", nm.EstimatedError print " # iterations:", nm.IterationsNeeded print " # function evaluations:", nm.EvaluationsNeeded

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