| Directory: | ./ |
|---|---|
| File: | include/crocoddyl/core/activations/2norm-barrier.hpp |
| Date: | 2025-01-16 08:47:40 |
| Exec | Total | Coverage | |
|---|---|---|---|
| Lines: | 32 | 40 | 80.0% |
| Branches: | 18 | 88 | 20.5% |
| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | /////////////////////////////////////////////////////////////////////////////// | ||
| 2 | // BSD 3-Clause License | ||
| 3 | // | ||
| 4 | // Copyright (C) 2021, LAAS-CNRS, Airbus, University of Edinburgh | ||
| 5 | // Copyright note valid unless otherwise stated in individual files. | ||
| 6 | // All rights reserved. | ||
| 7 | /////////////////////////////////////////////////////////////////////////////// | ||
| 8 | |||
| 9 | #ifndef CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 10 | #define CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 11 | |||
| 12 | #include <pinocchio/utils/static-if.hpp> | ||
| 13 | #include <stdexcept> | ||
| 14 | |||
| 15 | #include "crocoddyl/core/activation-base.hpp" | ||
| 16 | #include "crocoddyl/core/fwd.hpp" | ||
| 17 | #include "crocoddyl/core/utils/exception.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 | |||
| 47 | typedef _Scalar Scalar; | ||
| 48 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 49 | typedef ActivationModelAbstractTpl<Scalar> Base; | ||
| 50 | typedef ActivationDataAbstractTpl<Scalar> ActivationDataAbstract; | ||
| 51 | typedef ActivationData2NormBarrierTpl<Scalar> Data; | ||
| 52 | typedef typename MathBase::VectorXs VectorXs; | ||
| 53 | |||
| 54 | /** | ||
| 55 | * @brief Initialize the 2-norm barrier activation model | ||
| 56 | * | ||
| 57 | * The default `alpha` value is defined as 0.1. | ||
| 58 | * | ||
| 59 | * @param[in] nr Dimension of the residual vector | ||
| 60 | * @param[in] alpha Threshold factor (default 0.1) | ||
| 61 | * @param[in] true_hessian Boolean indicating whether to use the Gauss-Newton | ||
| 62 | * approximation or true Hessian in computing the derivatives (default: false) | ||
| 63 | */ | ||
| 64 | 185 | explicit ActivationModel2NormBarrierTpl(const std::size_t nr, | |
| 65 | const Scalar alpha = Scalar(0.1), | ||
| 66 | const bool true_hessian = false) | ||
| 67 | 185 | : Base(nr), alpha_(alpha), true_hessian_(true_hessian) { | |
| 68 |
1/2✗ Branch 0 not taken.
✓ Branch 1 taken 185 times.
|
185 | if (alpha < Scalar(0.)) { |
| 69 | ✗ | throw_pretty("Invalid argument: " << "alpha should be a positive value"); | |
| 70 | } | ||
| 71 | 185 | }; | |
| 72 | 374 | virtual ~ActivationModel2NormBarrierTpl() {}; | |
| 73 | |||
| 74 | /** | ||
| 75 | * @brief Compute the 2-norm barrier function | ||
| 76 | * | ||
| 77 | * @param[in] data 2-norm barrier activation data | ||
| 78 | * @param[in] r Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$ | ||
| 79 | */ | ||
| 80 | 3795 | virtual void calc(const boost::shared_ptr<ActivationDataAbstract>& data, | |
| 81 | const Eigen::Ref<const VectorXs>& r) { | ||
| 82 |
1/2✗ Branch 1 not taken.
✓ Branch 2 taken 3795 times.
|
3795 | if (static_cast<std::size_t>(r.size()) != nr_) { |
| 83 | ✗ | throw_pretty( | |
| 84 | "Invalid argument: " << "r has wrong dimension (it should be " + | ||
| 85 | std::to_string(nr_) + ")"); | ||
| 86 | } | ||
| 87 | 3795 | boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data); | |
| 88 | |||
| 89 |
1/2✓ Branch 1 taken 3795 times.
✗ Branch 2 not taken.
|
3795 | d->d = r.norm(); |
| 90 |
2/2✓ Branch 1 taken 158 times.
✓ Branch 2 taken 3637 times.
|
3795 | if (d->d < alpha_) { |
| 91 | 158 | data->a_value = Scalar(0.5) * (d->d - alpha_) * (d->d - alpha_); | |
| 92 | } else { | ||
| 93 | 3637 | data->a_value = Scalar(0.0); | |
| 94 | } | ||
| 95 | 3795 | }; | |
| 96 | |||
| 97 | /** | ||
| 98 | * @brief Compute the derivatives of the 2norm-barrier function | ||
| 99 | * | ||
| 100 | * @param[in] data 2-norm barrier activation data | ||
| 101 | * @param[in] r Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$ | ||
| 102 | */ | ||
| 103 | 212 | virtual void calcDiff(const boost::shared_ptr<ActivationDataAbstract>& data, | |
| 104 | const Eigen::Ref<const VectorXs>& r) { | ||
| 105 |
1/2✗ Branch 1 not taken.
✓ Branch 2 taken 212 times.
|
212 | if (static_cast<std::size_t>(r.size()) != nr_) { |
| 106 | ✗ | throw_pretty( | |
| 107 | "Invalid argument: " << "r has wrong dimension (it should be " + | ||
| 108 | std::to_string(nr_) + ")"); | ||
| 109 | } | ||
| 110 | 212 | boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data); | |
| 111 | |||
| 112 |
2/2✓ Branch 1 taken 6 times.
✓ Branch 2 taken 206 times.
|
212 | if (d->d < alpha_) { |
| 113 |
2/4✓ Branch 3 taken 6 times.
✗ Branch 4 not taken.
✓ Branch 7 taken 6 times.
✗ Branch 8 not taken.
|
6 | data->Ar = (d->d - alpha_) / d->d * r; |
| 114 |
1/2✗ Branch 0 not taken.
✓ Branch 1 taken 6 times.
|
6 | if (true_hessian_) { |
| 115 | ✗ | data->Arr.diagonal() = | |
| 116 | ✗ | alpha_ * r.array().square() / std::pow(d->d, 3); // True Hessian | |
| 117 | ✗ | data->Arr.diagonal().array() += (d->d - alpha_) / d->d; | |
| 118 | } else { | ||
| 119 |
3/6✓ Branch 1 taken 6 times.
✗ Branch 2 not taken.
✓ Branch 4 taken 6 times.
✗ Branch 5 not taken.
✓ Branch 9 taken 6 times.
✗ Branch 10 not taken.
|
6 | data->Arr.diagonal() = |
| 120 |
1/2✓ Branch 3 taken 6 times.
✗ Branch 4 not taken.
|
12 | r.array().square() / std::pow(d->d, 2); // GN Hessian approximation |
| 121 | } | ||
| 122 | } else { | ||
| 123 |
1/2✓ Branch 2 taken 206 times.
✗ Branch 3 not taken.
|
206 | data->Ar.setZero(); |
| 124 |
1/2✓ Branch 2 taken 206 times.
✗ Branch 3 not taken.
|
206 | data->Arr.setZero(); |
| 125 | } | ||
| 126 | 212 | }; | |
| 127 | |||
| 128 | /** | ||
| 129 | * @brief Create the 2norm-barrier activation data | ||
| 130 | * | ||
| 131 | * @return the activation data | ||
| 132 | */ | ||
| 133 | 4679 | virtual boost::shared_ptr<ActivationDataAbstract> createData() { | |
| 134 |
1/2✓ Branch 2 taken 4679 times.
✗ Branch 3 not taken.
|
4679 | return boost::allocate_shared<Data>(Eigen::aligned_allocator<Data>(), this); |
| 135 | }; | ||
| 136 | |||
| 137 | /** | ||
| 138 | * @brief Get and set the threshold factor | ||
| 139 | */ | ||
| 140 | ✗ | const Scalar& get_alpha() const { return alpha_; }; | |
| 141 | ✗ | void set_alpha(const Scalar& alpha) { alpha_ = alpha; }; | |
| 142 | |||
| 143 | /** | ||
| 144 | * @brief Print relevant information of the 2-norm barrier model | ||
| 145 | * | ||
| 146 | * @param[out] os Output stream object | ||
| 147 | */ | ||
| 148 | 37 | virtual void print(std::ostream& os) const { | |
| 149 | 37 | os << "ActivationModel2NormBarrier {nr=" << nr_ << ", alpha=" << alpha_ | |
| 150 | 37 | << ", Hessian=" << true_hessian_ << "}"; | |
| 151 | 37 | } | |
| 152 | |||
| 153 | protected: | ||
| 154 | using Base::nr_; //!< Dimension of the residual vector | ||
| 155 | Scalar alpha_; //!< Threshold factor | ||
| 156 | bool true_hessian_; //!< Use true Hessian in calcDiff if true, Gauss-Newton | ||
| 157 | //!< approximation if false | ||
| 158 | }; | ||
| 159 | |||
| 160 | template <typename _Scalar> | ||
| 161 | struct ActivationData2NormBarrierTpl | ||
| 162 | : public ActivationDataAbstractTpl<_Scalar> { | ||
| 163 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 164 | |||
| 165 | typedef _Scalar Scalar; | ||
| 166 | typedef ActivationDataAbstractTpl<Scalar> Base; | ||
| 167 | |||
| 168 | template <typename Activation> | ||
| 169 | 4679 | explicit ActivationData2NormBarrierTpl(Activation* const activation) | |
| 170 | 4679 | : Base(activation), d(Scalar(0)) {} | |
| 171 | |||
| 172 | Scalar d; //!< Norm of the residual | ||
| 173 | |||
| 174 | using Base::a_value; | ||
| 175 | using Base::Ar; | ||
| 176 | using Base::Arr; | ||
| 177 | }; | ||
| 178 | |||
| 179 | } // namespace crocoddyl | ||
| 180 | |||
| 181 | #endif // CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_ | ||
| 182 |