tsgGradientDescent.hpp

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/*
 * Copyright (c) 2022, Miroslav Stoyanov & Weiwei Kong
 *
 * This file is part of
 * Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
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#ifndef __TASMANIAN_GRADIENT_DESCENT_HPP
#define __TASMANIAN_GRADIENT_DESCENT_HPP
 
#include "tsgOptimizationUtils.hpp"
 
namespace TasOptimization {
 
class GradientDescentState {
  public:
    GradientDescentState() = delete;
    GradientDescentState(const std::vector<double> &x0, const double initial_stepsize) :
        adaptive_stepsize(initial_stepsize), x(x0) {};
 
    GradientDescentState(const GradientDescentState &source) = default;
    GradientDescentState(GradientDescentState &&source) = default;
 
    GradientDescentState& operator=(GradientDescentState &&source) = default;
    GradientDescentState& operator=(GradientDescentState &source) = default;
 
    inline operator std::vector<double>&() {return x;};
 
    inline size_t getNumDimensions() const {return x.size();}
    inline double getAdaptiveStepsize() const {return adaptive_stepsize;}
    inline void getX(double x_out[]) const {std::copy_n(x.begin(), x.size(), x_out);}
    inline std::vector<double> getX() const {return x;}
 
    inline void setAdaptiveStepsize(const double new_stepsize) {adaptive_stepsize = new_stepsize;}
    inline void setX(const double x_new[]) {std::copy_n(x_new, x.size(), x.begin());}
    inline void setX(const std::vector<double> &x_new) {
        checkVarSize("GradientDescentState::setCandidate", "candidate point", x_new.size(), x.size());
        x = x_new;
    }
 
    friend OptimizationStatus GradientDescent(const ObjectiveFunctionSingle &func, const GradientFunctionSingle &grad,
                                              const ProjectionFunctionSingle &proj, const double increase_coeff,
                                              const double decrease_coeff, const int max_iterations, const double tolerance,
                                              GradientDescentState &state);
    friend OptimizationStatus GradientDescent(const ObjectiveFunctionSingle &func, const GradientFunctionSingle &grad,
                                              const double increase_coeff, const double decrease_coeff, const int max_iterations,
                                              const double tolerance, GradientDescentState &state);
    friend OptimizationStatus GradientDescent(const GradientFunctionSingle &grad, const double stepsize, const int max_iterations,
                                              const double tolerance, std::vector<double> &state);
 
  private:
    double adaptive_stepsize;
    std::vector<double> x;
};
 
OptimizationStatus GradientDescent(const GradientFunctionSingle &grad, const double stepsize, const int max_iterations,
                                   const double tolerance, std::vector<double> &state);
 
OptimizationStatus GradientDescent(const ObjectiveFunctionSingle &func, const GradientFunctionSingle &grad,
                                   const double increase_coeff,  const double decrease_coeff, const int max_iterations,
                                   const double tolerance, GradientDescentState &state);
OptimizationStatus GradientDescent(const ObjectiveFunctionSingle &func, const GradientFunctionSingle &grad,
                                   const ProjectionFunctionSingle &proj, const double increase_coeff,
                                   const double decrease_coeff, const int max_iterations, const double tolerance,
                                   GradientDescentState &state);
 
}
 
#endif