Program Listing for File wrapper.hpp
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//
// Copyright (c) 2022 INRIA
//
#ifndef PROXSUITE_PROXQP_SPARSE_WRAPPER_HPP
#define PROXSUITE_PROXQP_SPARSE_WRAPPER_HPP
#include <proxsuite/proxqp/results.hpp>
#include <proxsuite/proxqp/settings.hpp>
#include <proxsuite/proxqp/sparse/solver.hpp>
#include <proxsuite/proxqp/sparse/helpers.hpp>
namespace proxsuite {
namespace proxqp {
namespace sparse {
template<typename T, typename I>
struct QP
{
Results<T> results;
Settings<T> settings;
Model<T, I> model;
Workspace<T, I> work;
preconditioner::RuizEquilibration<T, I> ruiz;
QP(isize dim, isize n_eq, isize n_in)
: results(dim, n_eq, n_in)
, settings()
, model(dim, n_eq, n_in)
, work()
, ruiz(dim, n_eq + n_in, 1e-3, 10, preconditioner::Symmetry::UPPER)
{
work.timer.stop();
work.internal.do_symbolic_fact = true;
work.internal.is_initialized = false;
}
QP(const SparseMat<bool, I>& H,
const SparseMat<bool, I>& A,
const SparseMat<bool, I>& C)
: QP(H.rows(), A.rows(), C.rows())
{
if (settings.compute_timings) {
work.timer.stop();
work.timer.start();
}
SparseMat<bool, I> H_triu = H.template triangularView<Eigen::Upper>();
SparseMat<bool, I> AT = A.transpose();
SparseMat<bool, I> CT = C.transpose();
proxsuite::linalg::sparse::MatRef<bool, I> Href = {
proxsuite::linalg::sparse::from_eigen, H_triu
};
proxsuite::linalg::sparse::MatRef<bool, I> ATref = {
proxsuite::linalg::sparse::from_eigen, AT
};
proxsuite::linalg::sparse::MatRef<bool, I> CTref = {
proxsuite::linalg::sparse::from_eigen, CT
};
work.setup_symbolic_factorizaton(
model, Href.symbolic(), ATref.symbolic(), CTref.symbolic());
if (settings.compute_timings) {
results.info.setup_time = work.timer.elapsed().user; // in microseconds
}
}
void init(optional<SparseMat<T, I>> H,
optional<VecRef<T>> g,
optional<SparseMat<T, I>> A,
optional<VecRef<T>> b,
optional<SparseMat<T, I>> C,
optional<VecRef<T>> l,
optional<VecRef<T>> u,
bool compute_preconditioner_ = true,
optional<T> rho = nullopt,
optional<T> mu_eq = nullopt,
optional<T> mu_in = nullopt)
{
if (settings.compute_timings) {
work.timer.stop();
work.timer.start();
}
if (g != nullopt && g.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
g.value().size(),
model.dim,
"the dimension wrt the primal variable x variable for initializing g "
"is not valid.");
} else {
g.reset();
}
if (b != nullopt && b.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
b.value().size(),
model.n_eq,
"the dimension wrt equality constrained variables for initializing b "
"is not valid.");
} else {
b.reset();
}
if (u != nullopt && u.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
u.value().size(),
model.n_in,
"the dimension wrt inequality constrained variables for initializing u "
"is not valid.");
} else {
u.reset();
}
if (l != nullopt && l.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
l.value().size(),
model.n_in,
"the dimension wrt inequality constrained variables for initializing l "
"is not valid.");
} else {
l.reset();
}
if (H != nullopt && H.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
H.value().rows(),
model.dim,
"the row dimension for initializing H is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
H.value().cols(),
model.dim,
"the column dimension for initializing H is not valid.");
} else {
H.reset();
}
if (A != nullopt && A.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
A.value().rows(),
model.n_eq,
"the row dimension for initializing A is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
A.value().cols(),
model.dim,
"the column dimension for initializing A is not valid.");
} else {
A.reset();
}
if (C != nullopt && C.value().size() != 0) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
C.value().rows(),
model.n_in,
"the row dimension for initializing C is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
C.value().cols(),
model.dim,
"the column dimension for initializing C is not valid.");
} else {
C.reset();
}
work.internal.proximal_parameter_update = false;
PreconditionerStatus preconditioner_status;
if (compute_preconditioner_) {
preconditioner_status = proxsuite::proxqp::PreconditionerStatus::EXECUTE;
} else {
preconditioner_status = proxsuite::proxqp::PreconditionerStatus::IDENTITY;
}
proxsuite::proxqp::sparse::update_proximal_parameters(
settings, results, work, rho, mu_eq, mu_in);
if (g != nullopt) {
model.g = g.value();
} // else qpmodel.g remains initialzed to a matrix with zero elements or
// zero shape
if (b != nullopt) {
model.b = b.value();
} // else qpmodel.b remains initialzed to a matrix with zero elements or
// zero shape
if (u != nullopt) {
model.u = u.value();
} // else qpmodel.u remains initialzed to a matrix with zero elements or
// zero shape
if (l != nullopt) {
model.l = l.value();
} // else qpmodel.l remains initialzed to a matrix with zero elements or
// zero shape
// avoid allocations when H is not nullopt
SparseMat<T, I> AT(model.dim, model.n_eq);
if (A != nullopt) {
AT = (A.value()).transpose();
} else {
AT.setZero();
}
SparseMat<T, I> CT(model.dim, model.n_in);
if (C != nullopt) {
CT = (C.value()).transpose();
} else {
CT.setZero();
}
if (H != nullopt) {
SparseMat<T, I> H_triu =
(H.value()).template triangularView<Eigen::Upper>();
sparse::QpView<T, I> qp = {
{ proxsuite::linalg::sparse::from_eigen, H_triu },
{ proxsuite::linalg::sparse::from_eigen, model.g },
{ proxsuite::linalg::sparse::from_eigen, AT },
{ proxsuite::linalg::sparse::from_eigen, model.b },
{ proxsuite::linalg::sparse::from_eigen, CT },
{ proxsuite::linalg::sparse::from_eigen, model.l },
{ proxsuite::linalg::sparse::from_eigen, model.u }
};
qp_setup(qp, results, model, work, settings, ruiz, preconditioner_status);
} else {
SparseMat<T, I> H_triu(model.dim, model.dim);
H_triu.setZero();
H_triu = (H.value()).template triangularView<Eigen::Upper>();
sparse::QpView<T, I> qp = {
{ proxsuite::linalg::sparse::from_eigen, H_triu },
{ proxsuite::linalg::sparse::from_eigen, model.g },
{ proxsuite::linalg::sparse::from_eigen, AT },
{ proxsuite::linalg::sparse::from_eigen, model.b },
{ proxsuite::linalg::sparse::from_eigen, CT },
{ proxsuite::linalg::sparse::from_eigen, model.l },
{ proxsuite::linalg::sparse::from_eigen, model.u }
};
qp_setup(qp, results, model, work, settings, ruiz, preconditioner_status);
}
work.internal.is_initialized = true;
if (settings.compute_timings) {
results.info.setup_time += work.timer.elapsed().user; // in microseconds
}
};
void update(const optional<SparseMat<T, I>> H,
optional<VecRef<T>> g,
const optional<SparseMat<T, I>> A,
optional<VecRef<T>> b,
const optional<SparseMat<T, I>> C,
optional<VecRef<T>> l,
optional<VecRef<T>> u,
bool update_preconditioner = true,
optional<T> rho = nullopt,
optional<T> mu_eq = nullopt,
optional<T> mu_in = nullopt)
{
if (!work.internal.is_initialized) {
init(H, g, A, b, C, l, u, update_preconditioner, rho, mu_eq, mu_in);
return;
}
if (settings.compute_timings) {
work.timer.stop();
work.timer.start();
}
work.internal.dirty = false;
work.internal.proximal_parameter_update = false;
PreconditionerStatus preconditioner_status;
if (update_preconditioner) {
preconditioner_status = proxsuite::proxqp::PreconditionerStatus::EXECUTE;
} else {
preconditioner_status = proxsuite::proxqp::PreconditionerStatus::KEEP;
}
isize n = model.dim;
isize n_eq = model.n_eq;
isize n_in = model.n_in;
proxsuite::linalg::sparse::MatMut<T, I> kkt_unscaled =
model.kkt_mut_unscaled();
auto kkt_top_n_rows = detail::top_rows_mut_unchecked(
proxsuite::linalg::veg::unsafe, kkt_unscaled, n);
proxsuite::linalg::sparse::MatMut<T, I> H_unscaled =
detail::middle_cols_mut(kkt_top_n_rows, 0, n, model.H_nnz);
proxsuite::linalg::sparse::MatMut<T, I> AT_unscaled =
detail::middle_cols_mut(kkt_top_n_rows, n, n_eq, model.A_nnz);
proxsuite::linalg::sparse::MatMut<T, I> CT_unscaled =
detail::middle_cols_mut(kkt_top_n_rows, n + n_eq, n_in, model.C_nnz);
// check the model is valid
if (g != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(g.value().size(),
model.dim,
"the dimension wrt the primal variable x "
"variable for updating g is not valid.");
}
if (b != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(b.value().size(),
model.n_eq,
"the dimension wrt equality constrained "
"variables for updating b is not valid.");
}
if (u != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(u.value().size(),
model.n_in,
"the dimension wrt inequality constrained "
"variables for updating u is not valid.");
}
if (l != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(l.value().size(),
model.n_in,
"the dimension wrt inequality constrained "
"variables for updating l is not valid.");
}
if (H != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
H.value().rows(),
model.dim,
"the row dimension for updating H is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
H.value().cols(),
model.dim,
"the column dimension for updating H is not valid.");
}
if (A != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
A.value().rows(),
model.n_eq,
"the row dimension for updating A is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
A.value().cols(),
model.dim,
"the column dimension for updating A is not valid.");
}
if (C != nullopt) {
PROXSUITE_CHECK_ARGUMENT_SIZE(
C.value().rows(),
model.n_in,
"the row dimension for updating C is not valid.");
PROXSUITE_CHECK_ARGUMENT_SIZE(
C.value().cols(),
model.dim,
"the column dimension for updating C is not valid.");
}
// update the model
if (g != nullopt) {
model.g = g.value();
}
if (b != nullopt) {
model.b = b.value();
}
if (u != nullopt) {
model.u = u.value();
}
if (l != nullopt) {
model.l = l.value();
}
if (H != nullopt) {
SparseMat<T, I> H_triu =
H.value().template triangularView<Eigen::Upper>();
if (A != nullopt) {
if (C != nullopt) {
bool res =
have_same_structure(
H_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen, H_triu }) &&
have_same_structure(AT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }) &&
have_same_structure(CT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(H_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
H_triu }); // copy rhs into lhs
copy(
AT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }); // copy rhs into lhs
copy(
CT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) }); // copy rhs into lhs
}
} else {
bool res =
have_same_structure(
H_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen, H_triu }) &&
have_same_structure(AT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(H_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
H_triu }); // copy rhs into lhs
copy(
AT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }); // copy rhs into lhs
}
}
} else if (C != nullopt) {
bool res =
have_same_structure(
H_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen, H_triu }) &&
have_same_structure(CT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(H_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
H_triu }); // copy rhs into lhs
copy(CT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) }); // copy rhs into lhs
}
} else {
bool res = have_same_structure(
H_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen, H_triu });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(H_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
H.value() }); // copy rhs into lhs
}
}
} else if (A != nullopt) {
if (C != nullopt) {
bool res =
have_same_structure(AT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }) &&
have_same_structure(CT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(AT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }); // copy rhs into lhs
copy(CT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) }); // copy rhs into lhs
}
} else {
bool res =
have_same_structure(AT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(AT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(A.value().transpose()) }); // copy rhs into lhs
}
}
} else if (C != nullopt) {
bool res =
have_same_structure(CT_unscaled.as_const(),
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) });
/* TO PUT IN DEBUG MODE
std::cout << "have same structure = " << res << std::endl;
*/
if (res) {
copy(CT_unscaled,
{ proxsuite::linalg::sparse::from_eigen,
SparseMat<T, I>(C.value().transpose()) }); // copy rhs into lhs
}
}
SparseMat<T, I> H_triu =
H_unscaled.to_eigen().template triangularView<Eigen::Upper>();
sparse::QpView<T, I> qp = {
{ proxsuite::linalg::sparse::from_eigen, H_triu },
{ proxsuite::linalg::sparse::from_eigen, model.g },
{ proxsuite::linalg::sparse::from_eigen, AT_unscaled.to_eigen() },
{ proxsuite::linalg::sparse::from_eigen, model.b },
{ proxsuite::linalg::sparse::from_eigen, CT_unscaled.to_eigen() },
{ proxsuite::linalg::sparse::from_eigen, model.l },
{ proxsuite::linalg::sparse::from_eigen, model.u }
};
proxsuite::proxqp::sparse::update_proximal_parameters(
settings, results, work, rho, mu_eq, mu_in);
qp_setup(qp,
results,
model,
work,
settings,
ruiz,
preconditioner_status); // store model value + performs scaling
// according to chosen options
if (settings.compute_timings) {
results.info.setup_time = work.timer.elapsed().user; // in microseconds
}
};
void solve()
{
qp_solve( //
results,
model,
settings,
work,
ruiz);
};
void solve(optional<VecRef<T>> x,
optional<VecRef<T>> y,
optional<VecRef<T>> z)
{
proxsuite::proxqp::sparse::warm_start(x, y, z, results, settings, model);
qp_solve( //
results,
model,
settings,
work,
ruiz);
};
void cleanup() { results.cleanup(settings); }
};
template<typename T, typename I>
proxqp::Results<T>
solve(
optional<SparseMat<T, I>> H,
optional<VecRef<T>> g,
optional<SparseMat<T, I>> A,
optional<VecRef<T>> b,
optional<SparseMat<T, I>> C,
optional<VecRef<T>> l,
optional<VecRef<T>> u,
optional<VecRef<T>> x = nullopt,
optional<VecRef<T>> y = nullopt,
optional<VecRef<T>> z = nullopt,
optional<T> eps_abs = nullopt,
optional<T> eps_rel = nullopt,
optional<T> rho = nullopt,
optional<T> mu_eq = nullopt,
optional<T> mu_in = nullopt,
optional<bool> verbose = nullopt,
bool compute_preconditioner = true,
bool compute_timings = false,
optional<isize> max_iter = nullopt,
proxsuite::proxqp::InitialGuessStatus initial_guess =
proxsuite::proxqp::InitialGuessStatus::EQUALITY_CONSTRAINED_INITIAL_GUESS,
proxsuite::proxqp::SparseBackend sparse_backend =
proxsuite::proxqp::SparseBackend::Automatic,
bool check_duality_gap = false,
optional<T> eps_duality_gap_abs = nullopt,
optional<T> eps_duality_gap_rel = nullopt)
{
isize n(0);
isize n_eq(0);
isize n_in(0);
if (H != nullopt) {
n = H.value().rows();
}
if (A != nullopt) {
n_eq = A.value().rows();
}
if (C != nullopt) {
n_in = C.value().rows();
}
proxqp::sparse::QP<T, I> Qp(n, n_eq, n_in);
Qp.settings.initial_guess = initial_guess;
Qp.settings.check_duality_gap = check_duality_gap;
if (eps_abs != nullopt) {
Qp.settings.eps_abs = eps_abs.value();
}
if (eps_rel != nullopt) {
Qp.settings.eps_rel = eps_rel.value();
}
if (verbose != nullopt) {
Qp.settings.verbose = verbose.value();
}
if (max_iter != nullopt) {
Qp.settings.max_iter = max_iter.value();
}
if (eps_duality_gap_abs != nullopt) {
Qp.settings.eps_duality_gap_abs = eps_duality_gap_abs.value();
}
if (eps_duality_gap_rel != nullopt) {
Qp.settings.eps_duality_gap_rel = eps_duality_gap_rel.value();
}
Qp.settings.compute_timings = compute_timings;
Qp.settings.sparse_backend = sparse_backend;
Qp.init(H, g, A, b, C, l, u, compute_preconditioner, rho, mu_eq, mu_in);
Qp.solve(x, y, z);
return Qp.results;
}
template<typename T, typename I>
struct BatchQP
{
std::vector<QP<T, I>> vector_qp;
sparse::isize m_size;
BatchQP(long unsigned int batchSize)
{
if (vector_qp.max_size() != batchSize) {
vector_qp.clear();
vector_qp.reserve(batchSize);
}
m_size = 0;
}
QP<T, I>& init_qp_in_place(sparse::isize dim,
sparse::isize n_eq,
sparse::isize n_in)
{
vector_qp.emplace_back(dim, n_eq, n_in);
auto& qp = vector_qp.back();
m_size++;
return qp;
};
// /*!
// * Init a QP in place and return a reference to it
// */
// QP<T, I>& init_qp_in_place(const sparse::SparseMat<bool, I>& H,
// const sparse::SparseMat<bool, I>& A,
// const sparse::SparseMat<bool, I>& C)
// {
// vector_qp.emplace_back(H.rows(), A.rows(), C.rows());
// auto& qp = vector_qp.back();
// m_size++;
// return qp;
// };
void insert(QP<T, I>& qp) { vector_qp.emplace_back(qp); };
QP<T, I>& get(isize i) { return vector_qp.at(i); };
QP<T, I>& operator[](isize i) { return vector_qp.at(i); };
sparse::isize size() { return m_size; };
};
} // namespace sparse
} // namespace proxqp
} // namespace proxsuite
#endif /* end of include guard PROXSUITE_PROXQP_SPARSE_WRAPPER_HPP */