gradient.hpp

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// Copyright (c) Sleipnir contributors
 
#pragma once
 
#include <utility>
 
#include <Eigen/SparseCore>
 
#include "sleipnir/autodiff/jacobian.hpp"
#include "sleipnir/autodiff/variable.hpp"
#include "sleipnir/autodiff/variable_matrix.hpp"
#include "sleipnir/util/concepts.hpp"
#include "sleipnir/util/symbol_exports.hpp"
 
namespace slp {
 
/**
 * This class calculates the gradient of a variable with respect to a vector of
 * variables.
 *
 * The gradient is only recomputed if the variable expression is quadratic or
 * higher order.
 */
class SLEIPNIR_DLLEXPORT Gradient {
 public:
  /**
   * Constructs a Gradient object.
   *
   * @param variable Variable of which to compute the gradient.
   * @param wrt Variable with respect to which to compute the gradient.
   */
  Gradient(Variable variable, Variable wrt)
      : m_jacobian{std::move(variable), std::move(wrt)} {}
 
  /**
   * Constructs a Gradient object.
   *
   * @param variable Variable of which to compute the gradient.
   * @param wrt Vector of variables with respect to which to compute the
   *   gradient.
   */
  Gradient(Variable variable, SleipnirMatrixLike auto wrt)
      : m_jacobian{VariableMatrix{std::move(variable)}, std::move(wrt)} {}
 
  /**
   * Returns the gradient as a VariableMatrix.
   *
   * This is useful when constructing optimization problems with derivatives in
   * them.
   *
   * @return The gradient as a VariableMatrix.
   */
  VariableMatrix get() const { return m_jacobian.get().T(); }
 
  /**
   * Evaluates the gradient at wrt's value.
   *
   * @return The gradient at wrt's value.
   */
  const Eigen::SparseVector<double>& value() {
    m_g = m_jacobian.value().transpose();
 
    return m_g;
  }
 
 private:
  Eigen::SparseVector<double> m_g;
 
  Jacobian m_jacobian;
};
 
}  // namespace slp