// // Math.NET Numerics, part of the Math.NET Project // http://numerics.mathdotnet.com // http://github.com/mathnet/mathnet-numerics // // Copyright (c) 2009-2017 Math.NET // // Permission is hereby granted, free of charge, to any person // obtaining a copy of this software and associated documentation // files (the "Software"), to deal in the Software without // restriction, including without limitation the rights to use, // copy, modify, merge, publish, distribute, sublicense, and/or sell // copies of the Software, and to permit persons to whom the // Software is furnished to do so, subject to the following // conditions: // // The above copyright notice and this permission notice shall be // included in all copies or substantial portions of the Software. // // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, // EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES // OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND // NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT // HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, // WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR // OTHER DEALINGS IN THE SOFTWARE. // using System; using IStation.Numerics.LinearAlgebra; namespace IStation.Numerics.Optimization.LineSearch { /// /// Search for a step size alpha that satisfies the weak Wolfe conditions. The weak Wolfe /// Conditions are /// i) Armijo Rule: f(x_k + alpha_k p_k) <= f(x_k) + c1 alpha_k p_k^T g(x_k) /// ii) Curvature Condition: p_k^T g(x_k + alpha_k p_k) >= c2 p_k^T g(x_k) /// where g(x) is the gradient of f(x), 0 < c1 < c2 < 1. /// /// Implementation is based on http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf /// /// references: /// http://en.wikipedia.org/wiki/Wolfe_conditions /// http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf /// public class WeakWolfeLineSearch : WolfeLineSearch { public WeakWolfeLineSearch(double c1, double c2, double parameterTolerance, int maxIterations = 10) : base(c1,c2,parameterTolerance,maxIterations) { // Validation in base class } protected override ExitCondition WolfeExitCondition => ExitCondition.WeakWolfeCriteria; protected override bool WolfeCondition(double stepDd, double initialDd) { return stepDd < C2 * initialDd; } protected override void ValidateValue(IObjectiveFunctionEvaluation eval) { if (!IsFinite(eval.Value)) { throw new EvaluationException(FormattableString.Invariant($"Non-finite value returned by objective function: {eval.Value}"), eval); } } protected override void ValidateInputArguments(IObjectiveFunctionEvaluation startingPoint, Vector searchDirection, double initialStep, double upperBound) { if (!startingPoint.IsGradientSupported) throw new ArgumentException("objective function does not support gradient"); } protected override void ValidateGradient(IObjectiveFunctionEvaluation eval) { foreach (double x in eval.Gradient) { if (!IsFinite(x)) { throw new EvaluationException(FormattableString.Invariant($"Non-finite value returned by gradient: {x}"), eval); } } } static bool IsFinite(double x) { return !(double.IsNaN(x) || double.IsInfinity(x)); } } }