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Merge pull request #271 from resibots/optimizers
Adding a few gradient-based optimizers
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,156 @@ | ||
| //| Copyright Inria May 2015 | ||
| //| This project has received funding from the European Research Council (ERC) under | ||
| //| the European Union's Horizon 2020 research and innovation programme (grant | ||
| //| agreement No 637972) - see http://www.resibots.eu | ||
| //| | ||
| //| Contributor(s): | ||
| //| - Jean-Baptiste Mouret ([email protected]) | ||
| //| - Antoine Cully ([email protected]) | ||
| //| - Konstantinos Chatzilygeroudis ([email protected]) | ||
| //| - Federico Allocati ([email protected]) | ||
| //| - Vaios Papaspyros ([email protected]) | ||
| //| - Roberto Rama ([email protected]) | ||
| //| | ||
| //| This software is a computer library whose purpose is to optimize continuous, | ||
| //| black-box functions. It mainly implements Gaussian processes and Bayesian | ||
| //| optimization. | ||
| //| Main repository: https://github.com/resibots/limbo | ||
| //| Documentation: http://www.resibots.eu/limbo | ||
| //| | ||
| //| This software is governed by the CeCILL-C license under French law and | ||
| //| abiding by the rules of distribution of free software. You can use, | ||
| //| modify and/ or redistribute the software under the terms of the CeCILL-C | ||
| //| license as circulated by CEA, CNRS and INRIA at the following URL | ||
| //| "http://www.cecill.info". | ||
| //| | ||
| //| As a counterpart to the access to the source code and rights to copy, | ||
| //| modify and redistribute granted by the license, users are provided only | ||
| //| with a limited warranty and the software's author, the holder of the | ||
| //| economic rights, and the successive licensors have only limited | ||
| //| liability. | ||
| //| | ||
| //| In this respect, the user's attention is drawn to the risks associated | ||
| //| with loading, using, modifying and/or developing or reproducing the | ||
| //| software by the user in light of its specific status of free software, | ||
| //| that may mean that it is complicated to manipulate, and that also | ||
| //| therefore means that it is reserved for developers and experienced | ||
| //| professionals having in-depth computer knowledge. Users are therefore | ||
| //| encouraged to load and test the software's suitability as regards their | ||
| //| requirements in conditions enabling the security of their systems and/or | ||
| //| data to be ensured and, more generally, to use and operate it in the | ||
| //| same conditions as regards security. | ||
| //| | ||
| //| The fact that you are presently reading this means that you have had | ||
| //| knowledge of the CeCILL-C license and that you accept its terms. | ||
| //| | ||
| #ifndef LIMBO_OPT_ADAM_HPP | ||
| #define LIMBO_OPT_ADAM_HPP | ||
|
|
||
| #include <algorithm> | ||
|
|
||
| #include <Eigen/Core> | ||
|
|
||
| #include <limbo/opt/optimizer.hpp> | ||
| #include <limbo/tools/macros.hpp> | ||
| #include <limbo/tools/math.hpp> | ||
|
|
||
| namespace limbo { | ||
| namespace defaults { | ||
| struct opt_adam { | ||
| /// @ingroup opt_defaults | ||
| /// number of max iterations | ||
| BO_PARAM(int, iterations, 300); | ||
|
|
||
| /// alpha - learning rate | ||
| BO_PARAM(double, alpha, 0.001); | ||
|
|
||
| /// β1 | ||
| BO_PARAM(double, b1, 0.9); | ||
|
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||
| /// β2 | ||
| BO_PARAM(double, b2, 0.999); | ||
|
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||
| /// norm epsilon for stopping | ||
| BO_PARAM(double, eps_stop, 0.0); | ||
| }; | ||
| } // namespace defaults | ||
| namespace opt { | ||
| /// @ingroup opt | ||
| /// Adam optimizer | ||
| /// Equations from: http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms | ||
| /// (I changed a bit the notation; η to α) | ||
| /// | ||
| /// Parameters: | ||
| /// - int iterations | ||
| /// - double alpha | ||
| /// - double b1 | ||
| /// - double b2 | ||
| /// - double eps_stop | ||
| template <typename Params> | ||
| struct Adam { | ||
| template <typename F> | ||
| Eigen::VectorXd operator()(const F& f, const Eigen::VectorXd& init, bool bounded) const | ||
| { | ||
| assert(Params::opt_adam::b1() >= 0. && Params::opt_adam::b1() < 1.); | ||
| assert(Params::opt_adam::b2() >= 0. && Params::opt_adam::b2() < 1.); | ||
| assert(Params::opt_adam::alpha() >= 0.); | ||
|
|
||
| size_t param_dim = init.size(); | ||
| double b1 = Params::opt_adam::b1(); | ||
| double b2 = Params::opt_adam::b2(); | ||
| double b1_t = b1; | ||
| double b2_t = b2; | ||
| double alpha = Params::opt_adam::alpha(); | ||
| double stop = Params::opt_adam::eps_stop(); | ||
| double epsilon = 1e-8; | ||
|
|
||
| Eigen::VectorXd m = Eigen::VectorXd::Zero(param_dim); | ||
| Eigen::VectorXd v = Eigen::VectorXd::Zero(param_dim); | ||
|
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| Eigen::VectorXd params = init; | ||
|
|
||
| if (bounded) { | ||
| for (int j = 0; j < params.size(); j++) { | ||
| if (params(j) < 0) | ||
| params(j) = 0; | ||
| if (params(j) > 1) | ||
| params(j) = 1; | ||
| } | ||
| } | ||
|
|
||
| for (int i = 0; i < Params::opt_adam::iterations(); ++i) { | ||
| Eigen::VectorXd prev_params = params; | ||
| auto perf = opt::eval_grad(f, params); | ||
|
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| Eigen::VectorXd grad = opt::grad(perf); | ||
| m = b1 * m.array() + (1. - b1) * grad.array(); | ||
| v = b2 * v.array() + (1. - b2) * grad.array().square(); | ||
|
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| Eigen::VectorXd m_hat = m.array() / (1. - b1_t); | ||
| Eigen::VectorXd v_hat = v.array() / (1. - b2_t); | ||
|
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| params.array() += alpha * m_hat.array() / (v_hat.array().sqrt() + epsilon); | ||
|
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| b1_t *= b1; | ||
| b2_t *= b2; | ||
|
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||
| if (bounded) { | ||
| for (int j = 0; j < params.size(); j++) { | ||
| if (params(j) < 0) | ||
| params(j) = 0; | ||
| if (params(j) > 1) | ||
| params(j) = 1; | ||
| } | ||
| } | ||
|
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| if ((prev_params - params).norm() < stop) | ||
| break; | ||
| } | ||
|
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| return params; | ||
| } | ||
| }; | ||
| } // namespace opt | ||
| } // namespace limbo | ||
|
|
||
| #endif |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,159 @@ | ||
| //| Copyright Inria May 2015 | ||
| //| This project has received funding from the European Research Council (ERC) under | ||
| //| the European Union's Horizon 2020 research and innovation programme (grant | ||
| //| agreement No 637972) - see http://www.resibots.eu | ||
| //| | ||
| //| Contributor(s): | ||
| //| - Jean-Baptiste Mouret ([email protected]) | ||
| //| - Antoine Cully ([email protected]) | ||
| //| - Konstantinos Chatzilygeroudis ([email protected]) | ||
| //| - Federico Allocati ([email protected]) | ||
| //| - Vaios Papaspyros ([email protected]) | ||
| //| - Roberto Rama ([email protected]) | ||
| //| | ||
| //| This software is a computer library whose purpose is to optimize continuous, | ||
| //| black-box functions. It mainly implements Gaussian processes and Bayesian | ||
| //| optimization. | ||
| //| Main repository: https://github.com/resibots/limbo | ||
| //| Documentation: http://www.resibots.eu/limbo | ||
| //| | ||
| //| This software is governed by the CeCILL-C license under French law and | ||
| //| abiding by the rules of distribution of free software. You can use, | ||
| //| modify and/ or redistribute the software under the terms of the CeCILL-C | ||
| //| license as circulated by CEA, CNRS and INRIA at the following URL | ||
| //| "http://www.cecill.info". | ||
| //| | ||
| //| As a counterpart to the access to the source code and rights to copy, | ||
| //| modify and redistribute granted by the license, users are provided only | ||
| //| with a limited warranty and the software's author, the holder of the | ||
| //| economic rights, and the successive licensors have only limited | ||
| //| liability. | ||
| //| | ||
| //| In this respect, the user's attention is drawn to the risks associated | ||
| //| with loading, using, modifying and/or developing or reproducing the | ||
| //| software by the user in light of its specific status of free software, | ||
| //| that may mean that it is complicated to manipulate, and that also | ||
| //| therefore means that it is reserved for developers and experienced | ||
| //| professionals having in-depth computer knowledge. Users are therefore | ||
| //| encouraged to load and test the software's suitability as regards their | ||
| //| requirements in conditions enabling the security of their systems and/or | ||
| //| data to be ensured and, more generally, to use and operate it in the | ||
| //| same conditions as regards security. | ||
| //| | ||
| //| The fact that you are presently reading this means that you have had | ||
| //| knowledge of the CeCILL-C license and that you accept its terms. | ||
| //| | ||
| #ifndef LIMBO_OPT_GRADIENT_ASCENT_HPP | ||
| #define LIMBO_OPT_GRADIENT_ASCENT_HPP | ||
|
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| #include <algorithm> | ||
|
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| #include <Eigen/Core> | ||
|
|
||
| #include <limbo/opt/optimizer.hpp> | ||
| #include <limbo/tools/macros.hpp> | ||
| #include <limbo/tools/math.hpp> | ||
|
|
||
| namespace limbo { | ||
| namespace defaults { | ||
| struct opt_gradient_ascent { | ||
| /// @ingroup opt_defaults | ||
| /// number of max iterations | ||
| BO_PARAM(int, iterations, 300); | ||
|
|
||
| /// alpha - learning rate | ||
| BO_PARAM(double, alpha, 0.001); | ||
|
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| /// gamma - for momentum | ||
| BO_PARAM(double, gamma, 0.0); | ||
|
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| /// nesterov momentum; turn on/off | ||
| BO_PARAM(bool, nesterov, false); | ||
|
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| /// norm epsilon for stopping | ||
| BO_PARAM(double, eps_stop, 0.0); | ||
| }; | ||
| } // namespace defaults | ||
| namespace opt { | ||
| /// @ingroup opt | ||
| /// Gradient Ascent with or without momentum (Nesterov or simple) | ||
| /// Equations from: http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms | ||
| /// (I changed a bit the notation; η to α) | ||
| /// | ||
| /// Parameters: | ||
| /// - int iterations | ||
| /// - double alpha | ||
| /// - double gamma | ||
| /// - bool nesterov | ||
| /// - double eps_stop | ||
| template <typename Params> | ||
| struct GradientAscent { | ||
| template <typename F> | ||
| Eigen::VectorXd operator()(const F& f, const Eigen::VectorXd& init, bool bounded) const | ||
| { | ||
| assert(Params::opt_gradient_ascent::gamma() >= 0. && Params::opt_gradient_ascent::gamma() < 1.); | ||
| assert(Params::opt_gradient_ascent::alpha() >= 0.); | ||
|
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| size_t param_dim = init.size(); | ||
| double gamma = Params::opt_gradient_ascent::gamma(); | ||
| double alpha = Params::opt_gradient_ascent::alpha(); | ||
| double stop = Params::opt_gradient_ascent::eps_stop(); | ||
| bool is_nesterov = Params::opt_gradient_ascent::nesterov(); | ||
|
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| Eigen::VectorXd v = Eigen::VectorXd::Zero(param_dim); | ||
|
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| Eigen::VectorXd params = init; | ||
|
|
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| if (bounded) { | ||
| for (int j = 0; j < params.size(); j++) { | ||
| if (params(j) < 0) | ||
| params(j) = 0; | ||
| if (params(j) > 1) | ||
| params(j) = 1; | ||
| } | ||
| } | ||
|
|
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| for (int i = 0; i < Params::opt_gradient_ascent::iterations(); ++i) { | ||
| Eigen::VectorXd prev_params = params; | ||
| Eigen::VectorXd query_params = params; | ||
| // if Nesterov momentum, change query parameters | ||
| if (is_nesterov) { | ||
| query_params.array() += gamma * v.array(); | ||
|
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| // make sure that the parameters are still in bounds, if needed | ||
| if (bounded) { | ||
| for (int j = 0; j < query_params.size(); j++) { | ||
| if (query_params(j) < 0) | ||
| query_params(j) = 0; | ||
| if (query_params(j) > 1) | ||
| query_params(j) = 1; | ||
| } | ||
| } | ||
| } | ||
| auto perf = opt::eval_grad(f, query_params); | ||
|
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| Eigen::VectorXd grad = opt::grad(perf); | ||
| v = gamma * v.array() + alpha * grad.array(); | ||
|
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| params.array() += v.array(); | ||
|
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| if (bounded) { | ||
| for (int j = 0; j < params.size(); j++) { | ||
| if (params(j) < 0) | ||
| params(j) = 0; | ||
| if (params(j) > 1) | ||
| params(j) = 1; | ||
| } | ||
| } | ||
|
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| if ((prev_params - params).norm() < stop) | ||
| break; | ||
| } | ||
|
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| return params; | ||
| } | ||
| }; | ||
| } // namespace opt | ||
| } // namespace limbo | ||
|
|
||
| #endif |
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