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File:	include/crocoddyl/core/activations/2norm-barrier.hpp	Lines:	32	42	76.2 %
Date:	2024-02-13 11:12:33	Branches:	20	92	21.7 %


///////////////////////////////////////////////////////////////////////////////
// BSD 3-Clause License
//
// Copyright (C) 2021, LAAS-CNRS, Airbus, University of Edinburgh
// Copyright note valid unless otherwise stated in individual files.
// All rights reserved.
///////////////////////////////////////////////////////////////////////////////

#ifndef CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_
#define CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_

#include <pinocchio/utils/static-if.hpp>
#include <stdexcept>

#include "crocoddyl/core/activation-base.hpp"
#include "crocoddyl/core/fwd.hpp"
#include "crocoddyl/core/utils/exception.hpp"

namespace crocoddyl {

/**
 * @brief 2-norm barrier activation
 *
 * This activation function describes a quadratic barrier of the 2-norm of a
 * residual vector, i.e.,
 * \f[
 * \Bigg\{\begin{aligned}
 * &\frac{1}{2} (d - \alpha)^2, &\textrm{if} \,\,\, d < \alpha \\
 * &0, &\textrm{otherwise},
 * \end{aligned}
 * \f]
 * where \f$d = \|r\|\f$ is the norm of the residual, \f$\alpha\f$ the threshold
 * distance from which the barrier is active, \f$nr\f$ is the dimension of the
 * residual vector.
 *
 * The computation of the function and it derivatives are carried out in
 * `calc()` and `calcDiff()`, respectively.
 *
 * \sa `ActivationModelAbstractTpl`, `calc()`, `calcDiff()`, `createData()`
 */
template <typename _Scalar>
class ActivationModel2NormBarrierTpl
    : public ActivationModelAbstractTpl<_Scalar> {
 public:
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW

  typedef _Scalar Scalar;
  typedef MathBaseTpl<Scalar> MathBase;
  typedef ActivationModelAbstractTpl<Scalar> Base;
  typedef ActivationDataAbstractTpl<Scalar> ActivationDataAbstract;
  typedef ActivationData2NormBarrierTpl<Scalar> Data;
  typedef typename MathBase::VectorXs VectorXs;

  /**
   * @brief Initialize the 2-norm barrier activation model
   *
   * The default `alpha` value is defined as 0.1.
   *
   * @param[in] nr            Dimension of the residual vector
   * @param[in] alpha         Threshold factor (default 0.1)
   * @param[in] true_hessian  Boolean indicating whether to use the Gauss-Newton
   * approximation or true Hessian in computing the derivatives (default: false)
   */
  explicit ActivationModel2NormBarrierTpl(const std::size_t nr,
                                          const Scalar alpha = Scalar(0.1),
                                          const bool true_hessian = false)
      : Base(nr), alpha_(alpha), true_hessian_(true_hessian) {
    if (alpha < Scalar(0.)) {
      throw_pretty("Invalid argument: "
                   << "alpha should be a positive value");
    }
  };
  virtual ~ActivationModel2NormBarrierTpl(){};

  /**
   * @brief Compute the 2-norm barrier function
   *
   * @param[in] data  2-norm barrier activation data
   * @param[in] r     Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$
   */
  virtual void calc(const boost::shared_ptr<ActivationDataAbstract>& data,
                    const Eigen::Ref<const VectorXs>& r) {
    if (static_cast<std::size_t>(r.size()) != nr_) {
      throw_pretty("Invalid argument: "
                   << "r has wrong dimension (it should be " +
                          std::to_string(nr_) + ")");
    }
    boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data);

    d->d = r.norm();
    if (d->d < alpha_) {
      data->a_value = Scalar(0.5) * (d->d - alpha_) * (d->d - alpha_);
    } else {
      data->a_value = Scalar(0.0);
    }
  };

  /**
   * @brief Compute the derivatives of the 2norm-barrier function
   *
   * @param[in] data  2-norm barrier activation data
   * @param[in] r     Residual vector \f$\mathbf{r}\in\mathbb{R}^{nr}\f$
   */
  virtual void calcDiff(const boost::shared_ptr<ActivationDataAbstract>& data,
                        const Eigen::Ref<const VectorXs>& r) {
    if (static_cast<std::size_t>(r.size()) != nr_) {
      throw_pretty("Invalid argument: "
                   << "r has wrong dimension (it should be " +
                          std::to_string(nr_) + ")");
    }
    boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data);

    if (d->d < alpha_) {
      data->Ar = (d->d - alpha_) / d->d * r;
      if (true_hessian_) {
        data->Arr.diagonal() =
            alpha_ * r.array().square() / std::pow(d->d, 3);  // True Hessian
        data->Arr.diagonal().array() += (d->d - alpha_) / d->d;
      } else {
        data->Arr.diagonal() =
            r.array().square() / std::pow(d->d, 2);  // GN Hessian approximation
      }
    } else {
      data->Ar.setZero();
      data->Arr.setZero();
    }
  };

  /**
   * @brief Create the 2norm-barrier activation data
   *
   * @return the activation data
   */
  virtual boost::shared_ptr<ActivationDataAbstract> createData() {
    return boost::allocate_shared<Data>(Eigen::aligned_allocator<Data>(), this);
  };

  /**
   * @brief Get and set the threshold factor
   */
  const Scalar& get_alpha() const { return alpha_; };
  void set_alpha(const Scalar& alpha) { alpha_ = alpha; };

  /**
   * @brief Print relevant information of the 2-norm barrier model
   *
   * @param[out] os  Output stream object
   */
  virtual void print(std::ostream& os) const {
    os << "ActivationModel2NormBarrier {nr=" << nr_ << ", alpha=" << alpha_
       << ", Hessian=" << true_hessian_ << "}";
  }

 protected:
  using Base::nr_;     //!< Dimension of the residual vector
  Scalar alpha_;       //!< Threshold factor
  bool true_hessian_;  //!< Use true Hessian in calcDiff if true, Gauss-Newton
                       //!< approximation if false
};

template <typename _Scalar>
struct ActivationData2NormBarrierTpl
    : public ActivationDataAbstractTpl<_Scalar> {
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW

  typedef _Scalar Scalar;
  typedef ActivationDataAbstractTpl<Scalar> Base;

  template <typename Activation>
  explicit ActivationData2NormBarrierTpl(Activation* const activation)
      : Base(activation), d(Scalar(0)) {}

  Scalar d;  //!< Norm of the residual

  using Base::a_value;
  using Base::Ar;
  using Base::Arr;
};

}  // namespace crocoddyl

#endif  // CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_


Generated by: GCOVR (Version 4.2)

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	✗✓	185	if (alpha < Scalar(0.)) {
69			throw_pretty("Invalid argument: "
70			<< "alpha should be a positive value");
71			}
72		185	};
73		374	virtual ~ActivationModel2NormBarrierTpl(){};
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		3795	virtual void calc(const boost::shared_ptr<ActivationDataAbstract>& data,
82			const Eigen::Ref<const VectorXs>& r) {
83	✓✗✗✓	3795	if (static_cast<std::size_t>(r.size()) != nr_) {
84			throw_pretty("Invalid argument: "
85			<< "r has wrong dimension (it should be " +
86			std::to_string(nr_) + ")");
87			}
88		7590	boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data);
89
90	✓✗	3795	d->d = r.norm();
91	✓✓	3795	if (d->d < alpha_) {
92		158	data->a_value = Scalar(0.5) * (d->d - alpha_) * (d->d - alpha_);
93			} else {
94		3637	data->a_value = Scalar(0.0);
95			}
96		3795	};
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		212	virtual void calcDiff(const boost::shared_ptr<ActivationDataAbstract>& data,
105			const Eigen::Ref<const VectorXs>& r) {
106	✓✗✗✓	212	if (static_cast<std::size_t>(r.size()) != nr_) {
107			throw_pretty("Invalid argument: "
108			<< "r has wrong dimension (it should be " +
109			std::to_string(nr_) + ")");
110			}
111		424	boost::shared_ptr<Data> d = boost::static_pointer_cast<Data>(data);
112
113	✓✓	212	if (d->d < alpha_) {
114	✓✗✓✗	6	data->Ar = (d->d - alpha_) / d->d * r;
115	✗✓	6	if (true_hessian_) {
116			data->Arr.diagonal() =
117			alpha_ * r.array().square() / std::pow(d->d, 3); // True Hessian
118			data->Arr.diagonal().array() += (d->d - alpha_) / d->d;
119			} else {
120	✓✗✓✗ ✓✗	6	data->Arr.diagonal() =
121	✓✗	12	r.array().square() / std::pow(d->d, 2); // GN Hessian approximation
122			}
123			} else {
124	✓✗	206	data->Ar.setZero();
125	✓✗	206	data->Arr.setZero();
126			}
127		212	};
128
129			/**
130			* @brief Create the 2norm-barrier activation data
131			*
132			* @return the activation data
133			*/
134		4679	virtual boost::shared_ptr<ActivationDataAbstract> createData() {
135	✓✗	4679	return boost::allocate_shared<Data>(Eigen::aligned_allocator<Data>(), this);
136			};
137
138			/**
139			* @brief Get and set the threshold factor
140			*/
141			const Scalar& get_alpha() const { return alpha_; };
142			void set_alpha(const Scalar& alpha) { alpha_ = alpha; };
143
144			/**
145			* @brief Print relevant information of the 2-norm barrier model
146			*
147			* @param[out] os Output stream object
148			*/
149		37	virtual void print(std::ostream& os) const {
150		37	os << "ActivationModel2NormBarrier {nr=" << nr_ << ", alpha=" << alpha_
151		37	<< ", Hessian=" << true_hessian_ << "}";
152		37	}
153
154			protected:
155			using Base::nr_; //!< Dimension of the residual vector
156			Scalar alpha_; //!< Threshold factor
157			bool true_hessian_; //!< Use true Hessian in calcDiff if true, Gauss-Newton
158			//!< approximation if false
159			};
160
161			template <typename _Scalar>
162			struct ActivationData2NormBarrierTpl
163			: public ActivationDataAbstractTpl<_Scalar> {
164			EIGEN_MAKE_ALIGNED_OPERATOR_NEW
165
166			typedef _Scalar Scalar;
167			typedef ActivationDataAbstractTpl<Scalar> Base;
168
169			template <typename Activation>
170		4679	explicit ActivationData2NormBarrierTpl(Activation* const activation)
171		4679	: Base(activation), d(Scalar(0)) {}
172
173			Scalar d; //!< Norm of the residual
174
175			using Base::a_value;
176			using Base::Ar;
177			using Base::Arr;
178			};
179
180			} // namespace crocoddyl
181
182			#endif // CROCODDYL_CORE_ACTIVATIONS_2NORM_BARRIER_HPP_