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/Profiler.hpp"
#include "sleipnir/autodiff/Variable.hpp"
#include "sleipnir/autodiff/VariableMatrix.hpp"
#include "sleipnir/util/SymbolExports.hpp"
 
namespace sleipnir {
 
/**
 * This class calculates the gradient of a 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) noexcept
      : Gradient{std::move(variable), VariableMatrix{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, const VariableMatrix& wrt) noexcept
      : m_jacobian{variable, wrt} {}
 
  /**
   * Returns the gradient as a VariableMatrix.
   *
   * This is useful when constructing optimization problems with derivatives in
   * them.
   */
  VariableMatrix Get() const { return m_jacobian.Get().T(); }
 
  /**
   * Evaluates the gradient at wrt's value.
   */
  const Eigen::SparseVector<double>& Value() {
    m_g = m_jacobian.Value();
 
    return m_g;
  }
 
  /**
   * Returns the profiler.
   */
  Profiler& GetProfiler() { return m_jacobian.GetProfiler(); }
 
 private:
  Eigen::SparseVector<double> m_g;
 
  Jacobian m_jacobian;
};
 
}  // namespace sleipnir