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jcarpentjcarpent
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Merge pull request #257 from Bambade/non_convex_qp
Estimate minimal eigenvalue of quadratic cost hessian
2 parents 41812e6 + 63fb999 commit 7374db8

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+1572
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‎README.md‎

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@@ -42,6 +42,7 @@ We are ready to integrate **ProxSuite** within other optimization ecosystems.
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- Python and Julia bindings for easy code prototyping without sacrificing performance.
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**Proxsuite** has a dedicated feature for solving batches of QPs.
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**Proxsuite** has a dedicated feature for solving nonconvex QPs.
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**Proxsuite** has a dedicated feature for solving the closest feasible QPs if they appear to be primal infeasible.
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**Proxsuite** is extensible.
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**Proxsuite** is reliable and extensively tested, showing the best performances on the hardest problems of the literature.

‎bindings/python/src/algorithms.hpp‎

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@@ -11,6 +11,7 @@
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#include "expose-qpobject.hpp"
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#include "expose-qpvector.hpp"
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#include "expose-solve.hpp"
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#include "expose-helpers.hpp"
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#ifdef PROXSUITE_PYTHON_INTERFACE_WITH_OPENMP
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#include "expose-parallel.hpp"
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#endif

‎bindings/python/src/expose-all.cpp‎

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@@ -35,6 +35,7 @@ exposeSparseAlgorithms(pybind11::module_ m)
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sparse::python::exposeQpObjectSparse<T, I>(m);
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sparse::python::exposeQPVectorSparse<T, I>(m);
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sparse::python::solveSparseQp<T, I>(m);
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sparse::python::exposeSparseHelpers<T, I>(m);
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}
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template<typename T>
@@ -45,6 +46,7 @@ exposeDenseAlgorithms(pybind11::module_ m)
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dense::python::exposeQpObjectDense<T>(m);
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dense::python::exposeQPVectorDense<T>(m);
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dense::python::solveDenseQp<T>(m);
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dense::python::exposeDenseHelpers<T>(m);
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}
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#ifdef PROXSUITE_PYTHON_INTERFACE_WITH_OPENMP

‎bindings/python/src/expose-helpers.hpp‎

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//
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// Copyright (c) 2022 INRIA
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//
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#include <pybind11/pybind11.h>
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#include <pybind11/eigen.h>
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#include <pybind11/stl.h>
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#include <proxsuite/proxqp/dense/helpers.hpp>
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#include <proxsuite/proxqp/sparse/helpers.hpp>
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namespace proxsuite {
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namespace proxqp {
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namespace dense {
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namespace python {
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template<typename T>
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void
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exposeDenseHelpers(pybind11::module_ m)
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{
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m.def("estimate_minimal_eigen_value_of_symmetric_matrix",
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&dense::estimate_minimal_eigen_value_of_symmetric_matrix<T>,
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"Function for estimating the minimal eigenvalue of a dense symmetric "
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"matrix. "
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"Two options are available: an exact method using "
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"SelfAdjointEigenSolver from Eigen, "
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"or a Power Iteration algorithm (with parameters : "
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"power_iteration_accuracy and nb_power_iteration).",
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pybind11::arg("H"),
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pybind11::arg_v("estimate_method_option",
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EigenValueEstimateMethodOption::ExactMethod,
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"Two options are available for "
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"estimating smallest eigenvalue: either a power "
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"iteration algorithm, or an exact method from Eigen."),
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pybind11::arg_v(
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"power_iteration_accuracy", T(1.E-3), "power iteration accuracy."),
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pybind11::arg_v("nb_power_iteration",
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1000,
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"maximal number of power iteration executed."));
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}
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} // namespace python
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} // namespace dense
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namespace sparse {
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namespace python {
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template<typename T, typename I>
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void
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exposeSparseHelpers(pybind11::module_ m)
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{
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m.def("estimate_minimal_eigen_value_of_symmetric_matrix",
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&sparse::estimate_minimal_eigen_value_of_symmetric_matrix<T, I>,
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"Function for estimating the minimal eigenvalue of a sparse symmetric "
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"matrix, "
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" using aPower Iteration algorithm (with parameters : "
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"power_iteration_accuracy and nb_power_iteration).",
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pybind11::arg("H"),
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pybind11::arg_v(
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"power_iteration_accuracy", T(1.E-3), "power iteration accuracy."),
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pybind11::arg_v("nb_power_iteration",
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1000,
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"maximal number of power iteration executed."));
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}
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} // namespace python
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} // namespace sparse
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} // namespace proxqp
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} // namespace proxsuite

‎bindings/python/src/expose-qpobject.hpp‎

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@@ -88,6 +88,7 @@ exposeQpObjectDense(pybind11::module_ m)
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bool compute_preconditioner,
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optional<T>,
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optional<T>,
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optional<T>,
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optional<T>)>(&dense::QP<T>::init),
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"function for initialize the QP model.",
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pybind11::arg_v("H", nullopt, "quadratic cost"),
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def("init",
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static_cast<void (dense::QP<T>::*)(optional<dense::MatRef<T>>,
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optional<dense::VecRef<T>>,
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bool compute_preconditioner,
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optional<T>,
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optional<T>,
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optional<T>,
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optional<T>)>(&dense::QP<T>::init),
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"function for initialize the QP model.",
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pybind11::arg_v("H", nullopt, "quadratic cost"),
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def("solve",
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static_cast<void (dense::QP<T>::*)()>(&dense::QP<T>::solve),
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"function used for solving the QP problem, using default parameters.")
@@ -163,6 +173,7 @@ exposeQpObjectDense(pybind11::module_ m)
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bool update_preconditioner,
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optional<T>,
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optional<T>,
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optional<T>,
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optional<T>)>(&dense::QP<T>::update),
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"function used for updating matrix or vector entry of the model using "
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"dense matrix entries.",
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def(
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"update",
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static_cast<void (dense::QP<T>::*)(optional<dense::MatRef<T>>,
@@ -198,6 +213,7 @@ exposeQpObjectDense(pybind11::module_ m)
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bool update_preconditioner,
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optional<T>,
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optional<T>,
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optional<T>,
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optional<T>)>(&dense::QP<T>::update),
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"function used for updating matrix or vector entry of the model using "
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"dense matrix entries.",
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def("cleanup",
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&dense::QP<T>::cleanup,
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"function used for cleaning the workspace and result "
@@ -297,7 +317,11 @@ exposeQpObjectSparse(pybind11::module_ m)
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def("update",
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&sparse::QP<T, I>::update,
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pybind11::arg_v(
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"mu_eq", nullopt, "dual equality constraint proximal parameter"),
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pybind11::arg_v(
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"mu_in", nullopt, "dual inequality constraint proximal parameter"))
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"mu_in", nullopt, "dual inequality constraint proximal parameter"),
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pybind11::arg_v("manual_minimal_H_eigenvalue",
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nullopt,
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"manual minimal H eigenvalue proposed to regularize H"
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" in case it is non convex."))
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.def("solve",
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static_cast<void (sparse::QP<T, I>::*)()>(&sparse::QP<T, I>::solve),
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"function used for solving the QP problem, using default parameters.")

‎bindings/python/src/expose-results.hpp‎

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@@ -51,7 +51,12 @@ exposeResults(pybind11::module_ m)
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.def_readwrite("sparse_backend",
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&Info<T>::sparse_backend,
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"Sparse backend used to solve the qp, either SparseCholesky "
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"or MatrixFree.");
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"or MatrixFree.")
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.def_readwrite("minimal_H_eigenvalue_estimate",
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&Info<T>::minimal_H_eigenvalue_estimate,
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"By default it equals 0, in order to get an estimate, set "
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"appropriately the setting option "
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"find_H_minimal_eigenvalue.");
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::pybind11::class_<Results<T>>(m, "Results", pybind11::module_local())
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.def(::pybind11::init<i64, i64, i64>(),

‎bindings/python/src/expose-settings.hpp‎

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.value("MatrixFree", SparseBackend::MatrixFree)
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.value("SparseCholesky", SparseBackend::SparseCholesky)
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.export_values();
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::pybind11::enum_<EigenValueEstimateMethodOption>(
45+
m, "EigenValueEstimateMethodOption", pybind11::module_local())
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.value("PowerIteration", EigenValueEstimateMethodOption::PowerIteration)
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.value("ExactMethod", EigenValueEstimateMethodOption::ExactMethod)
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.export_values();
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::pybind11::class_<Settings<T>>(m, "Settings", pybind11::module_local())
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.def(::pybind11::init(), "Default constructor.") // constructor
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&Settings<T>::primal_infeasibility_solving)
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.def_readwrite("frequence_infeasibility_check",
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&Settings<T>::frequence_infeasibility_check)
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.def_readwrite("default_H_eigenvalue_estimate",
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&Settings<T>::default_H_eigenvalue_estimate)
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.def(pybind11::self == pybind11::self)
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.def(pybind11::self != pybind11::self)
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.def(pybind11::pickle(

‎bindings/python/src/expose-solve.hpp‎

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@@ -43,7 +43,8 @@ solveDenseQp(pybind11::module_ m)
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bool,
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optional<T>,
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optional<T>,
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bool>(&dense::solve<T>),
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bool,
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optional<T>>(&dense::solve<T>),
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"Function for solving a QP problem using PROXQP sparse backend directly "
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"without defining a QP object. It is possible to set up some of the solver "
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"parameters (warm start, initial guess option, proximal step sizes, "
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pybind11::arg_v("primal_infeasibility_solving",
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false,
105106
"solves the closest feasible problem in L2 sense "
106-
"if the QP problem appears to be infeasible."));
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"if the QP problem appears to be infeasible."),
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pybind11::arg_v("default_H_eigenvalue_estimate",
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0.,
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"Default estimate of the minimal eigen value of H."));
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108112
m.def(
109113
"solve",
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132136
bool,
133137
optional<T>,
134138
optional<T>,
135-
bool>(&dense::solve<T>),
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bool,
140+
optional<T>>(&dense::solve<T>),
136141
"Function for solving a QP problem using PROXQP sparse backend directly "
137142
"without defining a QP object. It is possible to set up some of the solver "
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"parameters (warm start, initial guess option, proximal step sizes, "
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pybind11::arg_v("primal_infeasibility_solving",
195200
false,
196201
"solves the closest feasible problem in L2 sense "
197-
"if the QP problem appears to be infeasible."));
202+
"if the QP problem appears to be infeasible."),
203+
pybind11::arg_v("default_H_eigenvalue_estimate",
204+
0.,
205+
"Default estimate of the minimal eigen value of H."));
198206
}
199207

200208
} // namespace python
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pybind11::arg_v("primal_infeasibility_solving",
270278
false,
271279
"solves the closest feasible problem in L2 sense "
272-
"if the QP problem appears to be infeasible."));
280+
"if the QP problem appears to be infeasible."),
281+
pybind11::arg_v("default_H_eigenvalue_estimate",
282+
0.,
283+
"Default estimate of the minimal eigen value of H."));
273284
}
274285

275286
} // namespace python

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