| Directory: | ./ |
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| File: | include/crocoddyl/core/activations/2norm-barrier.hpp |
| Date: | 2025-03-26 19:23:43 |
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| Lines: | 33 | 46 | 71.7% |
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| 1 | /////////////////////////////////////////////////////////////////////////////// | ||
| 2 | // BSD 3-Clause License | ||
| 3 | // | ||
| 4 | // Copyright (C) 2021-2025, LAAS-CNRS, Airbus, University of Edinburgh, | ||
| 5 | // Heriot-Watt University | ||
| 6 | // Copyright note valid unless otherwise stated in individual files. | ||
| 7 | // All rights reserved. | ||
| 8 | /////////////////////////////////////////////////////////////////////////////// | ||
| 9 | |||
| 10 | #ifndef CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 11 | #define CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 12 | |||
| 13 | #include <pinocchio/utils/static-if.hpp> | ||
| 14 | #include <stdexcept> | ||
| 15 | |||
| 16 | #include "crocoddyl/core/activation-base.hpp" | ||
| 17 | #include "crocoddyl/core/fwd.hpp" | ||
| 18 | |||
| 19 | namespace crocoddyl { | ||
| 20 | |||
| 21 | /** | ||
| 22 | * @brief 2-norm barrier activation | ||
| 23 | * | ||
| 24 | * This activation function describes a quadratic barrier of the 2-norm of a | ||
| 25 | * residual vector, i.e., | ||
| 26 | * \f[ | ||
| 27 | * \Bigg\{\begin{aligned} | ||
| 28 | * &\frac{1}{2} (d - \alpha)^2, &\textrm{if} \,\,\, d < \alpha \\ | ||
| 29 | * &0, &\textrm{otherwise}, | ||
| 30 | * \end{aligned} | ||
| 31 | * \f] | ||
| 32 | * where \f$d = \|r\|\f$ is the norm of the residual, \f$\alpha\f$ the threshold | ||
| 33 | * distance from which the barrier is active, \f$nr\f$ is the dimension of the | ||
| 34 | * residual vector. | ||
| 35 | * | ||
| 36 | * The computation of the function and it derivatives are carried out in | ||
| 37 | * `calc()` and `calcDiff()`, respectively. | ||
| 38 | * | ||
| 39 | * \sa `ActivationModelAbstractTpl`, `calc()`, `calcDiff()`, `createData()` | ||
| 40 | */ | ||
| 41 | template <typename _Scalar> | ||
| 42 | class ActivationModel2NormBarrierTpl | ||
| 43 | : public ActivationModelAbstractTpl<_Scalar> { | ||
| 44 | public: | ||
| 45 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 46 | 16 | CROCODDYL_DERIVED_CAST(ActivationModelBase, ActivationModel2NormBarrierTpl) | |
| 47 | |||
| 48 | typedef _Scalar Scalar; | ||
| 49 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 50 | typedef ActivationModelAbstractTpl<Scalar> Base; | ||
| 51 | typedef ActivationDataAbstractTpl<Scalar> ActivationDataAbstract; | ||
| 52 | typedef ActivationData2NormBarrierTpl<Scalar> Data; | ||
| 53 | typedef typename MathBase::VectorXs VectorXs; | ||
| 54 | |||
| 55 | /** | ||
| 56 | * @brief Initialize the 2-norm barrier activation model | ||
| 57 | * | ||
| 58 | * The default `alpha` value is defined as 0.1. | ||
| 59 | * | ||
| 60 | * @param[in] nr Dimension of the residual vector | ||
| 61 | * @param[in] alpha Threshold factor (default 0.1) | ||
| 62 | * @param[in] true_hessian Boolean indicating whether to use the Gauss-Newton | ||
| 63 | * approximation or true Hessian in computing the derivatives (default: false) | ||
| 64 | */ | ||
| 65 | 189 | explicit ActivationModel2NormBarrierTpl(const std::size_t nr, | |
| 66 | const Scalar alpha = Scalar(0.1), | ||
| 67 | const bool true_hessian = false) | ||
| 68 | 189 | : Base(nr), alpha_(alpha), true_hessian_(true_hessian) { | |
| 69 |
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189 | if (alpha < Scalar(0.)) { |
| 70 | ✗ | throw_pretty("Invalid argument: " << "alpha should be a positive value"); | |
| 71 | } | ||
| 72 | 189 | }; | |
| 73 | 390 | virtual ~ActivationModel2NormBarrierTpl() = default; | |
| 74 | |||
| 75 | /** | ||
| 76 | * @brief Compute the 2-norm barrier function | ||
| 77 | * | ||
| 78 | * @param[in] data 2-norm barrier activation data | ||
| 79 | * @param[in] r Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$ | ||
| 80 | */ | ||
| 81 | 3797 | virtual void calc(const std::shared_ptr<ActivationDataAbstract>& data, | |
| 82 | const Eigen::Ref<const VectorXs>& r) override { | ||
| 83 |
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3797 | if (static_cast<std::size_t>(r.size()) != nr_) { |
| 84 | ✗ | throw_pretty( | |
| 85 | "Invalid argument: " << "r has wrong dimension (it should be " + | ||
| 86 | std::to_string(nr_) + ")"); | ||
| 87 | } | ||
| 88 | 3797 | std::shared_ptr<Data> d = std::static_pointer_cast<Data>(data); | |
| 89 | |||
| 90 |
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3797 | d->d = r.norm(); |
| 91 |
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3797 | if (d->d < alpha_) { |
| 92 | 1 | data->a_value = Scalar(0.5) * (d->d - alpha_) * (d->d - alpha_); | |
| 93 | } else { | ||
| 94 | 3796 | data->a_value = Scalar(0.0); | |
| 95 | } | ||
| 96 | 3797 | }; | |
| 97 | |||
| 98 | /** | ||
| 99 | * @brief Compute the derivatives of the 2norm-barrier function | ||
| 100 | * | ||
| 101 | * @param[in] data 2-norm barrier activation data | ||
| 102 | * @param[in] r Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$ | ||
| 103 | */ | ||
| 104 | 213 | virtual void calcDiff(const std::shared_ptr<ActivationDataAbstract>& data, | |
| 105 | const Eigen::Ref<const VectorXs>& r) override { | ||
| 106 |
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213 | if (static_cast<std::size_t>(r.size()) != nr_) { |
| 107 | ✗ | throw_pretty( | |
| 108 | "Invalid argument: " << "r has wrong dimension (it should be " + | ||
| 109 | std::to_string(nr_) + ")"); | ||
| 110 | } | ||
| 111 | 213 | std::shared_ptr<Data> d = std::static_pointer_cast<Data>(data); | |
| 112 | |||
| 113 |
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213 | if (d->d < alpha_) { |
| 114 | ✗ | data->Ar = (d->d - alpha_) / d->d * r; | |
| 115 | ✗ | if (true_hessian_) { | |
| 116 | ✗ | data->Arr.diagonal() = | |
| 117 | ✗ | alpha_ * r.array().square() / pow(d->d, Scalar(3)); // True Hessian | |
| 118 | ✗ | data->Arr.diagonal().array() += (d->d - alpha_) / d->d; | |
| 119 | } else { | ||
| 120 | ✗ | data->Arr.diagonal() = | |
| 121 | ✗ | r.array().square() / | |
| 122 | ✗ | pow(d->d, Scalar(2)); // GN Hessian approximation | |
| 123 | } | ||
| 124 | } else { | ||
| 125 |
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213 | data->Ar.setZero(); |
| 126 |
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213 | data->Arr.setZero(); |
| 127 | } | ||
| 128 | 213 | }; | |
| 129 | |||
| 130 | /** | ||
| 131 | * @brief Create the 2norm-barrier activation data | ||
| 132 | * | ||
| 133 | * @return the activation data | ||
| 134 | */ | ||
| 135 | 4682 | virtual std::shared_ptr<ActivationDataAbstract> createData() override { | |
| 136 |
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4682 | return std::allocate_shared<Data>(Eigen::aligned_allocator<Data>(), this); |
| 137 | }; | ||
| 138 | |||
| 139 | template <typename NewScalar> | ||
| 140 | 4 | ActivationModel2NormBarrierTpl<NewScalar> cast() const { | |
| 141 | typedef ActivationModel2NormBarrierTpl<NewScalar> ReturnType; | ||
| 142 | 4 | ReturnType res(nr_, scalar_cast<NewScalar>(alpha_), true_hessian_); | |
| 143 | 4 | return res; | |
| 144 | } | ||
| 145 | |||
| 146 | /** | ||
| 147 | * @brief Get and set the threshold factor | ||
| 148 | */ | ||
| 149 | ✗ | const Scalar& get_alpha() const { return alpha_; }; | |
| 150 | ✗ | void set_alpha(const Scalar& alpha) { alpha_ = alpha; }; | |
| 151 | |||
| 152 | /** | ||
| 153 | * @brief Print relevant information of the 2-norm barrier model | ||
| 154 | * | ||
| 155 | * @param[out] os Output stream object | ||
| 156 | */ | ||
| 157 | 37 | virtual void print(std::ostream& os) const override { | |
| 158 | 37 | os << "ActivationModel2NormBarrier {nr=" << nr_ << ", alpha=" << alpha_ | |
| 159 | 37 | << ", Hessian=" << true_hessian_ << "}"; | |
| 160 | 37 | } | |
| 161 | |||
| 162 | protected: | ||
| 163 | using Base::nr_; //!< Dimension of the residual vector | ||
| 164 | Scalar alpha_; //!< Threshold factor | ||
| 165 | bool true_hessian_; //!< Use true Hessian in calcDiff if true, Gauss-Newton | ||
| 166 | //!< approximation if false | ||
| 167 | }; | ||
| 168 | |||
| 169 | template <typename _Scalar> | ||
| 170 | struct ActivationData2NormBarrierTpl | ||
| 171 | : public ActivationDataAbstractTpl<_Scalar> { | ||
| 172 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 173 | |||
| 174 | typedef _Scalar Scalar; | ||
| 175 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 176 | typedef typename MathBase::VectorXs VectorXs; | ||
| 177 | typedef typename MathBase::DiagonalMatrixXs DiagonalMatrixXs; | ||
| 178 | typedef ActivationDataAbstractTpl<Scalar> Base; | ||
| 179 | |||
| 180 | template <typename Activation> | ||
| 181 | 4682 | explicit ActivationData2NormBarrierTpl(Activation* const activation) | |
| 182 | 4682 | : Base(activation), d(Scalar(0)) {} | |
| 183 | 9364 | virtual ~ActivationData2NormBarrierTpl() = default; | |
| 184 | |||
| 185 | Scalar d; //!< Norm of the residual | ||
| 186 | |||
| 187 | using Base::a_value; | ||
| 188 | using Base::Ar; | ||
| 189 | using Base::Arr; | ||
| 190 | }; | ||
| 191 | |||
| 192 | } // namespace crocoddyl | ||
| 193 | |||
| 194 | CROCODDYL_DECLARE_EXTERN_TEMPLATE_CLASS( | ||
| 195 | crocoddyl::ActivationModel2NormBarrierTpl) | ||
| 196 | CROCODDYL_DECLARE_EXTERN_TEMPLATE_STRUCT( | ||
| 197 | crocoddyl::ActivationData2NormBarrierTpl) | ||
| 198 | |||
| 199 | #endif // CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 200 |