workspace.hpp

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#pragma once
 
#include "proxsuite-nlp/problem-base.hpp"
#include "proxsuite-nlp/ldlt-allocator.hpp"
 
#include <fmt/ostream.h>
 
namespace proxsuite {
namespace nlp {
 
template <typename Scalar>
auto allocate_ldlt_from_problem(const ProblemTpl<Scalar> &prob,
                                LDLTChoice choice) {
  std::vector<isize> nduals(prob.getNumConstraints());
  for (std::size_t i = 0; i < nduals.size(); ++i)
    nduals[i] = prob.getConstraintDim(i);
  return allocate_ldlt_from_sizes<Scalar>({prob.ndx()}, nduals, choice);
}
 
template <typename Scalar> struct WorkspaceTpl {
public:
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
 
  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
  using Problem = ProblemTpl<Scalar>;
 
 
  long nx;
  long ndx;
  std::size_t numblocks; // number of constraint blocks
  long numdual;          // total constraint dim
 
  MatrixXs kkt_matrix;
  VectorXs kkt_rhs;
  VectorXs kkt_rhs_corr;
  VectorXs kkt_err;
  VectorXs pd_step;
  VectorRef prim_step;
  VectorRef dual_step;
  Eigen::VectorXi signature;
 
  LDLTVariant<Scalar> ldlt_;
 
 
  VectorXs x_prev;
  VectorXs x_trial;
  VectorXs data_lams_prev;
  VectorXs data_lams_trial;
  VectorOfRef lams_prev;
  VectorOfRef lams_trial;
 
  VectorXs prox_grad;
  MatrixXs prox_hess;
 
 
  VectorXs dual_residual;
 
  VectorXs data_cstr_values;
  std::vector<VectorRef> cstr_values;
 
  Scalar objective_value;
  VectorXs objective_gradient;
  MatrixXs objective_hessian;
  VectorXs merit_gradient;
  VectorXs merit_dual_gradient;
 
  MatrixXs data_jacobians;
  MatrixXs data_hessians;
  MatrixXs data_jacobians_proj;
  std::vector<MatrixRef> cstr_jacobians;
  std::vector<MatrixRef> cstr_vector_hessian_prod;
  std::vector<MatrixRef> cstr_jacobians_proj;
 
  VectorXs data_shift_cstr_values;
  VectorXs data_lams_plus;
  VectorXs data_lams_plus_reproj;
  VectorXs data_lams_pdal;
  VectorXs data_lams_pdal_reproj;
  VectorXs data_shift_cstr_pdal;
 
  std::vector<VectorRef> lams_plus;
  std::vector<VectorRef> lams_plus_reproj;
  std::vector<VectorRef> shift_cstr_values;
  std::vector<VectorRef> lams_pdal;
  std::vector<VectorRef> lams_pdal_reproj;
  std::vector<VectorRef> shift_cstr_pdal;
 
  std::vector<Scalar> ls_alphas;
  std::vector<Scalar> ls_values;
  Scalar alpha_opt;
  Scalar dmerit_dir = 0.;
 
  VectorXs tmp_dx_scaled;
 
  WorkspaceTpl(const Problem &prob, LDLTChoice ldlt_choice = LDLTChoice::DENSE)
      : nx(long(prob.nx())), ndx(long(prob.ndx())),
        numblocks(prob.getNumConstraints()),
        numdual(prob.getTotalConstraintDim()),
        kkt_matrix(ndx + numdual, ndx + numdual), kkt_rhs(ndx + numdual),
        kkt_rhs_corr(ndx + numdual), kkt_err(kkt_rhs), pd_step(ndx + numdual),
        prim_step(pd_step.head(ndx)), dual_step(pd_step.tail(numdual)),
        signature(ndx + numdual),
        ldlt_(allocate_ldlt_from_problem(prob, ldlt_choice)), x_prev(nx),
        x_trial(nx), data_lams_prev(numdual), data_lams_trial(numdual),
        prox_grad(ndx), prox_hess(ndx, ndx), dual_residual(ndx),
        data_cstr_values(numdual), objective_gradient(ndx),
        objective_hessian(ndx, ndx), merit_gradient(ndx),
        merit_dual_gradient(numdual), data_jacobians(numdual, ndx),
        data_hessians((long)numblocks * ndx, ndx), data_lams_plus(numdual),
        data_lams_plus_reproj(numdual), data_lams_pdal(numdual),
        tmp_dx_scaled(ndx) {
    init(prob);
  }
 
  void init(const Problem &prob) {
    kkt_matrix.setZero();
    kkt_rhs.setZero();
    kkt_rhs_corr.setZero();
    pd_step.setZero();
    signature.setZero();
 
    x_prev.setZero();
    x_trial.setZero();
    helpers::allocateMultipliersOrResiduals(prob, data_lams_prev, lams_prev);
    helpers::allocateMultipliersOrResiduals(prob, data_lams_trial, lams_trial);
    prox_grad.setZero();
    prox_hess.setZero();
 
    dual_residual.setZero();
    helpers::allocateMultipliersOrResiduals(
        prob, data_cstr_values, cstr_values); // not multipliers but same dims
 
    objective_gradient.setZero();
    objective_hessian.setZero();
    merit_gradient.setZero();
    merit_dual_gradient.setZero();
    data_jacobians.setZero();
    data_hessians.setZero();
 
    helpers::allocateMultipliersOrResiduals(prob, data_shift_cstr_values,
                                            shift_cstr_values);
    helpers::allocateMultipliersOrResiduals(prob, data_lams_plus, lams_plus);
    helpers::allocateMultipliersOrResiduals(prob, data_lams_plus_reproj,
                                            lams_plus_reproj);
    helpers::allocateMultipliersOrResiduals(prob, data_lams_pdal, lams_pdal);
    helpers::allocateMultipliersOrResiduals(prob, data_lams_pdal_reproj,
                                            lams_pdal_reproj);
    helpers::allocateMultipliersOrResiduals(prob, data_shift_cstr_pdal,
                                            shift_cstr_pdal);
    tmp_dx_scaled.setZero();
 
    cstr_jacobians.reserve(numblocks);
    cstr_vector_hessian_prod.reserve(numblocks);
 
    data_jacobians_proj = data_jacobians;
 
    int cursor = 0;
    int nr = 0;
    for (std::size_t i = 0; i < numblocks; i++) {
      cursor = prob.getIndex(i);
      nr = prob.getConstraintDim(i);
      cstr_jacobians.emplace_back(data_jacobians.middleRows(cursor, nr));
      cstr_jacobians_proj.emplace_back(
          data_jacobians_proj.middleRows(cursor, nr));
      cstr_vector_hessian_prod.emplace_back(
          data_hessians.middleRows((int)i * ndx, ndx));
    }
  }
};
 
} // namespace nlp
} // namespace proxsuite
 
template <typename Scalar>
struct fmt::formatter<proxsuite::nlp::WorkspaceTpl<Scalar>>
    : fmt::ostream_formatter {};
 
#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
#include "proxsuite-nlp/workspace.txx"
#endif