// // 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.ObjectiveFunctions { internal class LazyObjectiveFunction : IObjectiveFunction { readonly Func, double> _function; readonly Func, Vector> _gradient; readonly Func, Matrix> _hessian; Vector _point; bool _hasFunctionValue; double _functionValue; bool _hasGradientValue; Vector _gradientValue; bool _hasHessianValue; Matrix _hessianValue; public LazyObjectiveFunction(Func, double> function, Func, Vector> gradient = null, Func, Matrix> hessian = null) { _function = function; _gradient = gradient; _hessian = hessian; IsGradientSupported = gradient != null; IsHessianSupported = hessian != null; } public IObjectiveFunction CreateNew() { return new LazyObjectiveFunction(_function, _gradient, _hessian); } public IObjectiveFunction Fork() { // no need to deep-clone values since they are replaced on evaluation return new LazyObjectiveFunction(_function, _gradient, _hessian) { _point = _point, _hasFunctionValue = _hasFunctionValue, _functionValue = _functionValue, _hasGradientValue = _hasGradientValue, _gradientValue = _gradientValue, _hasHessianValue = _hasHessianValue, _hessianValue = _hessianValue }; } public bool IsGradientSupported { get; private set; } public bool IsHessianSupported { get; private set; } public void EvaluateAt(Vector point) { _point = point; _hasFunctionValue = false; _hasGradientValue = false; _hasHessianValue = false; // don't keep references unnecessarily _gradientValue = null; _hessianValue = null; } public Vector Point => _point; public double Value { get { if (!_hasFunctionValue) { _functionValue = _function(_point); _hasFunctionValue = true; } return _functionValue; } } public Vector Gradient { get { if (!_hasGradientValue) { _gradientValue = _gradient(_point); _hasGradientValue = true; } return _gradientValue; } } public Matrix Hessian { get { if (!_hasHessianValue) { _hessianValue = _hessian(_point); _hasHessianValue = true; } return _hessianValue; } } } }