solver-qpoases

A qpOASES wrapper for Python that ships prebuilt binaries. It exposes qpOASES' online active-set solver through a thin, numpy-friendly, MATLAB-quadprog-like interface.

pip install marinholab-solvers-qpoases

Overview

Given a symmetric matrix H, a vector f, and (optionally) inequality and equality constraint matrices, the solver solves the quadratic program

min_x   0.5 * x' H x + f' x
s.t.    A x <= b
        Aeq x = beq

Once a problem has been solved once, subsequent solves on the same Solver instance are warm-started by default (Configuration.use_hotstart = True), which is the main performance benefit of qpOASES for repeated, related QPs.

Quickstart

import numpy as np
from marinholab.solvers import qpoases

solver = qpoases.Solver()

H = np.eye(2)                          # positive definite Hessian
f = np.array([-1.0, -1.0])             # linear term
A = np.array([[1.0, 0.0]])             # x[0] <= 0.2
b = np.array([0.2])

x = solver.solve_quadratic_program(H, f, A, b,
                                   Aeq=np.zeros((1, 2)),
                                   beq=np.zeros((1,)))
# x ≈ [0.2, 1.0]

Omitting constraints

Any of the four constraint arguments (A, b, Aeq, beq) can be None, meaning "no such constraint". The matrix and its right-hand side must be omitted (or provided) together:

# Unconstrained
x = solver.solve_quadratic_program(H, f, None, None, None, None)

# Equality constraints only
x = solver.solve_quadratic_program(H, f, None, None, Aeq, beq)

Active set

solver.get_active_set() reports, for each row of the combined constraint matrix (rows of A followed by rows of Aeq), whether it is:

  • -1 active at its lower bound,
  • 0 inactive,
  • +1 active at its upper bound (equality constraints are always +1, since their bounds coincide).
solver.solve_quadratic_program(H, f, A, b, Aeq, beq)
solver.get_active_set()  # e.g. [ 1.0, 0.0, 0.0]

Configuration

All of qpOASES' Options fields are exposed, plus a few wrapper-specific settings. Create a Configuration, tweak the fields you need, and pass it to the solver:

config = qpoases.Configuration()
config.hessian_type = qpoases.HessianType.HST_SEMIDEF   # H is rank-deficient
config.enableRegularisation = qpoases.BooleanType.BT_FALSE
config.termination_tolerance = 1.0e-9                    # tighter convergence
solver = qpoases.Solver(config)

The enum types are re-exported for convenience: qpoases.BooleanType, qpoases.HessianType, qpoases.PrintLevel, and qpoases.SubjectToStatus.

Wrapper-specific options

Option Default Type Description
maximum_working_set_recalculations 150 int Max working-set recalculations during the initial homotopy (nWSR passed to init/hotstart). Increase if solves hit the maximum.
use_hotstart True bool Warm-start subsequent solves with hotstart() instead of re-initialising with init().
hessian_type HST_POSDEF HessianType Definiteness assumed for H; given to the underlying SQProblem.

Hessian definiteness (HessianType)

Value Meaning
HST_ZERO Hessian is the zero matrix (LP formulation)
HST_IDENTITY Hessian is the identity matrix
HST_POSDEF Hessian is (strictly) positive definite
HST_POSDEF_NULLSPACE Positive definite on the null space of active bounds/constraints
HST_SEMIDEF Positive semi-definite
HST_INDEF Indefinite
HST_UNKNOWN Unknown

qpOASES options

These map 1:1 onto qpOASES' Options fields. Defaults match qpOASES' own defaults for a double-precision build (see Options::setToDefault()), with two deliberate exceptions noted inline: enableNZCTests and enableFlippingBounds default to BT_FALSE here (qpOASES' default is BT_TRUE) because those are the recommended "fast"/MPC settings for repeated, online solves. See the qpOASES manual for a full description of each option.

Booleans (BooleanType: BT_FALSE / BT_TRUE)

Option Default Description
enable_ramping BT_TRUE Enables the ramping strategy.
enable_far_bounds BT_TRUE Enables the far bounds strategy.
enableFlippingBounds BT_FALSE Allows flipping active bounds between lower and upper values. (differs from qpOASES default)
enableRegularisation BT_FALSE Regularises H when (semi-)definiteness is detected.
enable_full_li_tests BT_FALSE Uses the condition-hardened linear-independence (LI) test.
enableNZCTests BT_FALSE Enables the nonzero-curvature test. (differs from qpOASES default)
enable_equalities BT_FALSE Treats equality constraints as always active.
enable_inertia_correction BT_TRUE Repairs the working set when negative curvature is found during a hotstart.
enable_drop_infeasibles BT_FALSE Whether infeasible constraints may be dropped.

Integers (int_t)

Option Default Description
enable_drift_correction 1 Frequency of drift corrections (0 = off).
enable_cholesky_refactorisation 0 Frequency of full Cholesky refactorisation of the projected Hessian (0 = rank updates only).
num_regularisation_steps 0 Max successive regularisation steps.
num_refinement_steps 1 Max iterative-refinement steps.
drop_bound_priority 1 Priority used when dropping bounds.
drop_eq_con_priority 1 Priority used when dropping equality constraints.
drop_ineq_con_priority 1 Priority used when dropping inequality constraints.

Reals (real_t, double)

Option Default Description
termination_tolerance 5.0e6 * EPS (~1.1e-9) Relative tolerance that stops the homotopy. Smaller = more accurate, more work.
bound_tolerance 1.0e6 * EPS Bound tolerance; a constraint whose bounds differ by less is treated as an equality.
bound_relaxation 1.0e4 Offset for relaxing bounds at the start of the initial homotopy (also the initial far-bound value).
eps_num -1.0e3 * EPS Numerator tolerance for the ratio test.
eps_den 1.0e3 * EPS Denominator tolerance for the ratio test.
max_primal_jump 1.0e8 Max allowed primal jump in nonzero-curvature tests.
max_dual_jump 1.0e8 Max allowed dual jump in LI tests.
initial_ramping 0.5 Start value of the ramping strategy.
final_ramping 1.0 Final value of the ramping strategy.
initial_far_bounds 1.0e6 Initial size of the far bounds.
grow_far_bounds 1.0e3 Growth factor applied to the far bounds.
eps_flipping 1.0e3 * EPS Tolerance of the squared Cholesky diagonal factor that triggers flipping a bound.
eps_regularisation 1.0e3 * EPS Scaling factor of the identity matrix used for Hessian regularisation.
eps_iter_ref 1.0e2 * EPS Early-termination tolerance for iterative refinement.
eps_li_tests 1.0e5 * EPS Tolerance for the linear-independence tests.
eps_nzc_tests 3.0e3 * EPS Tolerance for the nonzero-curvature tests.
rcond_s_min 1.0e-14 Min reciprocal condition number of the Schur complement before a refactorisation is triggered.

Status / print enums

Option Default Type Description
print_level PL_LOW PrintLevel Verbosity of qpOASES output (PL_NONE, PL_LOW, PL_MEDIUM, PL_HIGH, PL_TABULAR, PL_DEBUG_ITER). Defaults to PL_LOW so the solver stays quiet (qpOASES' own default is PL_MEDIUM).
initial_status_bounds ST_LOWER SubjectToStatus Status assumed for all bounds at the first iteration.

Print levels (PrintLevel)

PL_DEBUG_ITER, PL_TABULAR, PL_NONE, PL_LOW, PL_MEDIUM, PL_HIGH.

Bound/constraint statuses (SubjectToStatus)

ST_LOWER, ST_INACTIVE, ST_UPPER, ST_INFEASIBLE_LOWER, ST_INFEASIBLE_UPPER, ST_UNDEFINED.

Examples

  • marinholab/solvers/qpoases/example.py — positive-definite and semi-definite solves, the None-constraint path, and the active set. Run it with qpoases_example (installed as a console script).
  • marinholab/solvers/qpoases/example_kinematics.py — an optional example showing the solver used in a hierarchical (task-priority) controller for a kinematically redundant robot, built on dqrobotics. It requires the optional dependencies dqrobotics and dqrobotics-pyplot (pip install --pre dqrobotics dqrobotics-pyplot).

Building from source

The package builds a C++ extension (via CMake + pybind11) and vendors qpOASES and pybind11 as git submodules.

git clone --recurse-submodules <repo>
pip install .

Prerequisites: a C++23 compiler, CMake, an Eigen3 installation, and Python. On Ubuntu: sudo apt-get install cmake libeigen3-dev.

Type checking

The package ships a type stub (marinholab/solvers/qpoases/_core.pyi) and a py.typed marker, so downstream projects can be checked with Pyright (or Pylance) without extra configuration. Run the project's own check with:

pyright

License

The qpOASES library is LGPLv2.1; the wrapper is under the terms of the included LICENSE file.

Key dates

PyPI data
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  • Released:
Latest release

Owner

PyPI data
Data sourced directly from PyPI's database.

Credits

Author/Maintainer: Murilo M. Marinho

Requires

Python >=3.9

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The following attestation bundles were made for marinholab_solvers_qpoases-26.4.0.39-cp310-cp310-macosx_26_0_universal2.whl:

Publisher: python-publish.yml on MarinhoLab/solver-qpoases

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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26.4.0.39 This release

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