linesearch-armijo.hpp

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#pragma once
 
#include "proxsuite-nlp/linesearch-base.hpp"
#include "proxsuite-nlp/exceptions.hpp"
#include "proxsuite-nlp/context.hpp"
 
#include <Eigen/QR>
 
namespace proxsuite {
namespace nlp {
 
template <typename T> struct PolynomialTpl {
  using VectorXs = typename math_types<T>::VectorXs;
  VectorXs coeffs;
  PolynomialTpl() {}
  PolynomialTpl(const Eigen::Ref<const VectorXs> &c) : coeffs(c) {}
  Eigen::Index degree() const { return coeffs.size() - 1; }
  inline T evaluate(T a) const {
    T r = 0.0;
    for (int i = 0; i < coeffs.size(); i++) {
      r = r * a + coeffs(i);
    }
    return r;
  }
  PolynomialTpl derivative() const {
    if (degree() == 0) {
      return PolynomialTpl(VectorXs::Zero(1));
    }
    VectorXs out(degree());
    for (int i = 0; i < coeffs.size() - 1; i++) {
      out(i) = coeffs(i) * (T(degree()) - i);
    }
    return PolynomialTpl(out);
  }
};
 
template <typename Scalar>
class ArmijoLinesearch final : public Linesearch<Scalar> {
public:
  using Base = Linesearch<Scalar>;
  using Base::options_;
  using FunctionSample = typename Base::FunctionSample;
  using Polynomial = PolynomialTpl<Scalar>;
  using VectorXs = typename math_types<Scalar>::VectorXs;
  using Matrix2s = Eigen::Matrix<Scalar, 2, 2>;
  using Vector2s = Eigen::Matrix<Scalar, 2, 1>;
 
  ArmijoLinesearch(const typename Base::Options &options) : Base(options) {}
 
  using fun_t = std::function<Scalar(Scalar)>;
 
  Scalar run(fun_t phi, const Scalar phi0, const Scalar dphi0,
             Scalar &alpha_try) {
    const FunctionSample lower_bound(0., phi0, dphi0);
 
    alpha_try = 1.;
    FunctionSample latest;
    FunctionSample previous;
 
    // try the full step; if failure encountered, aggressively
    // backtrack until no exception is raised
    while (true) {
      try {
        latest = FunctionSample(alpha_try, phi(alpha_try));
        break;
      } catch (const std::runtime_error &e) {
        alpha_try *= 0.5;
        if (alpha_try <= options_.alpha_min) {
          alpha_try = options_.alpha_min;
          break;
        }
      }
    }
 
    if (std::abs(dphi0) < options_.dphi_thresh) {
      return latest.phi;
    }
 
    for (std::size_t i = 0; i < options_.max_num_steps; i++) {
 
      const Scalar dM = latest.phi - phi0;
      if (dM <= options_.armijo_c1 * alpha_try * dphi0) {
        break;
      }
 
      // compute next alpha try
      LSInterpolation strat = options_.interp_type;
      if (strat == LSInterpolation::BISECTION) {
        alpha_try *= 0.5;
      } else {
        samples.reserve(3);
        samples = {lower_bound};
 
        // interpolation routines
        switch (strat) {
        case LSInterpolation::QUADRATIC: {
          // 2-point interp: value, derivative at 0 and latest value
          samples.push_back(latest);
          break;
        }
        case LSInterpolation::CUBIC: {
          // 3-point interp: phi(0), phi'(0) and last two values
          samples.push_back(latest);
          if (previous.valid) {
            samples.push_back(previous);
          }
          break;
        }
        default:
          PROXSUITE_NLP_RUNTIME_ERROR(
              "Unrecognized interpolation type in this context.\n");
          break;
        }
 
        alpha_try = this->minimize_interpolant(
            strat, options_.contraction_min * alpha_try,
            options_.contraction_max * alpha_try);
      }
 
      if (std::isnan(alpha_try)) {
        // handle NaN case
        alpha_try = options_.contraction_min * previous.alpha;
      } else {
        alpha_try = std::max(alpha_try, options_.alpha_min);
      }
 
      try {
        previous = latest;
        latest = FunctionSample(alpha_try, phi(alpha_try));
      } catch (const std::runtime_error &e) {
        continue;
      }
 
      if (alpha_try <= options_.alpha_min) {
        break;
      }
    }
    alpha_try = std::max(alpha_try, options_.alpha_min);
    return latest.phi;
  }
 
  Scalar minimize_interpolant(LSInterpolation strat, Scalar min_step_size,
                              Scalar max_step_size) {
    Scalar anext = 0.0;
    VectorXs &coeffs = interpolant.coeffs;
 
    assert(samples.size() >= 2);
    const FunctionSample &lower_bound = samples[0];
    const Scalar &phi0 = lower_bound.phi;
    const Scalar &dphi0 = lower_bound.dphi;
 
    if (samples.size() == 2) {
      strat = LSInterpolation::QUADRATIC;
    }
 
    switch (strat) {
    case LSInterpolation::QUADRATIC: {
      assert(samples.size() >= 2);
      const FunctionSample &cand0 = samples[1];
      Scalar a = (cand0.phi - phi0 - cand0.alpha * dphi0) /
                 (cand0.alpha * cand0.alpha);
      coeffs.conservativeResize(3);
      coeffs << a, dphi0, phi0;
      assert(interpolant.degree() == 2);
      anext = -dphi0 / (2. * a);
      break;
    }
    case LSInterpolation::CUBIC: {
      assert(samples.size() >= 3);
      const FunctionSample &cand0 = samples[1];
      const FunctionSample &cand1 = samples[2];
      const Scalar &a0 = cand0.alpha;
      const Scalar &a1 = cand1.alpha;
      Matrix2s alph_mat;
      Vector2s coeffs_cubic_interpolant;
      alph_mat(0, 0) = a0 * a0 * a0;
      alph_mat(0, 1) = a0 * a0;
      alph_mat(1, 0) = a1 * a1 * a1;
      alph_mat(1, 1) = a1 * a1;
 
      Vector2s alph_rhs{cand1.phi - phi0 - dphi0 * a1,
                        cand0.phi - phi0 - dphi0 * a0};
 
      Eigen::HouseholderQR<Matrix2s> decomp(alph_mat);
      coeffs_cubic_interpolant = decomp.solve(alph_rhs);
 
      const Scalar c3 = coeffs_cubic_interpolant(0);
      const Scalar c2 = coeffs_cubic_interpolant(1);
      coeffs.conservativeResize(4);
      coeffs << c3, c2, dphi0, phi0;
      assert(interpolant.degree() == 3);
 
      // minimizer of cubic interpolant -> solve dinterp/da = 0
      anext = (-c2 + std::sqrt(c2 * c2 - 3.0 * c3 * dphi0)) / (3.0 * c3);
      break;
    }
    default:
      break;
    }
 
    if ((anext > max_step_size) || (anext < min_step_size)) {
      // if min outside of (amin; amax), look at the edges
      Scalar pleft = interpolant.evaluate(min_step_size);
      Scalar pright = interpolant.evaluate(max_step_size);
      if (pleft < pright) {
        anext = min_step_size;
      } else {
        anext = max_step_size;
      }
    }
 
    return anext;
  }
 
protected:
  Polynomial interpolant;
  std::vector<FunctionSample> samples; // interpolation samples
};
 
#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
extern template struct PROXSUITE_NLP_EXPLICIT_INSTANTIATION_DECLARATION_DLLAPI
    PolynomialTpl<context::Scalar>;
extern template class PROXSUITE_NLP_EXPLICIT_INSTANTIATION_DECLARATION_DLLAPI
    ArmijoLinesearch<context::Scalar>;
#endif
 
} // namespace nlp
} // namespace proxsuite