Christoph Ruegg
9 years ago
8 changed files with 179 additions and 16 deletions
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// <copyright file="FindMinimum.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2017 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using System; |
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using MathNet.Numerics.LinearAlgebra; |
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using MathNet.Numerics.Optimization; |
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using MathNet.Numerics.Optimization.ObjectiveFunctions; |
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namespace MathNet.Numerics |
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{ |
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public static class FindMinimum |
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{ |
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/// <summary>
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/// Find value x that minimizes the scalar function f(x), constrained within bounds, using the Golden Section algorithm.
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/// For more options and diagnostics consider to use <see cref="GoldenSectionMinimizer"/> directly.
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/// </summary>
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public static double OfScalarFunctionConstrained(Func<double, double> function, double lowerBound, double upperBound, double tolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = new SimpleObjectiveFunction1D(function); |
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var algorithm = new GoldenSectionMinimizer(tolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, lowerBound, upperBound); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
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/// For more options and diagnostics consider to use <see cref="NelderMeadSimplex"/> directly.
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/// </summary>
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public static Vector<double> OfFunction(Func<Vector<double>, double> function, Vector<double> initialGuess, double tolerance=1e-8, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Value(function); |
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var algorithm = new NelderMeadSimplex(tolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
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/// The missing gradient is evaluated numerically (forward difference).
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/// For more options and diagnostics consider to use <see cref="BfgsBMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionConstrained(Func<Vector<double>, double> function, Vector<double> lowerBound, Vector<double> upperBound, Vector<double> initialGuess, double gradientTolerance=1e-5, double parameterTolerance=1e-5, double functionProgressTolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Value(function); |
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var objectiveWithGradient = new ForwardDifferenceGradientObjectiveFunction(objective, lowerBound, upperBound); |
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var algorithm = new BfgsBMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations); |
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var result = algorithm.FindMinimum(objectiveWithGradient, lowerBound, upperBound, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x) using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.
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/// For more options and diagnostics consider to use <see cref="BfgsMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradient(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient, Vector<double> initialGuess, double gradientTolerance=1e-5, double parameterTolerance=1e-5, double functionProgressTolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Gradient(function, gradient); |
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var algorithm = new BfgsMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x) using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.
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/// For more options and diagnostics consider to use <see cref="BfgsMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradient(Func<Vector<double>, Tuple<double, Vector<double>>> functionGradient, Vector<double> initialGuess, double gradientTolerance=1e-5, double parameterTolerance=1e-5, double functionProgressTolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Gradient(functionGradient); |
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var algorithm = new BfgsMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
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/// For more options and diagnostics consider to use <see cref="BfgsBMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradientConstrained(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient, Vector<double> lowerBound, Vector<double> upperBound, Vector<double> initialGuess, double gradientTolerance=1e-5, double parameterTolerance=1e-5, double functionProgressTolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Gradient(function, gradient); |
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var algorithm = new BfgsBMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, lowerBound, upperBound, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
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/// For more options and diagnostics consider to use <see cref="BfgsBMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradientConstrained(Func<Vector<double>, Tuple<double, Vector<double>>> functionGradient, Vector<double> lowerBound, Vector<double> upperBound, Vector<double> initialGuess, double gradientTolerance=1e-5, double parameterTolerance=1e-5, double functionProgressTolerance=1e-5, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.Gradient(functionGradient); |
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var algorithm = new BfgsBMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, lowerBound, upperBound, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x) using the Newton algorithm.
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/// For more options and diagnostics consider to use <see cref="NewtonMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradientHessian(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient, Func<Vector<double>, Matrix<double>> hessian, Vector<double> initialGuess, double tolerance=1e-8, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.GradientHessian(function, gradient, hessian); |
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var algorithm = new NewtonMinimizer(tolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, initialGuess); |
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return result.MinimizingPoint; |
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} |
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/// <summary>
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/// Find vector x that minimizes the function f(x) using the Newton algorithm.
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/// For more options and diagnostics consider to use <see cref="NewtonMinimizer"/> directly.
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/// </summary>
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public static Vector<double> OfFunctionGradientHessian(Func<Vector<double>, Tuple<double, Vector<double>, Matrix<double>>> functionGradientHessian, Vector<double> initialGuess, double tolerance=1e-8, int maxIterations=1000) |
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{ |
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var objective = ObjectiveFunction.GradientHessian(functionGradientHessian); |
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var algorithm = new NewtonMinimizer(tolerance, maxIterations); |
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var result = algorithm.FindMinimum(objective, initialGuess); |
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return result.MinimizingPoint; |
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} |
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} |
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} |
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