proxsuite package from proxsuite repo

proxsuite

Package Summary

Tags No category tags.
Version 0.6.3
License BSD-2
Build type CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/Simple-Robotics/proxsuite.git
VCS Type git
VCS Version devel
Last Updated 2024-02-13
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

The Advanced Proximal Optimization Toolbox

Additional Links

Maintainers

  • Justin Carpentier
  • Wolfgang Merkt

Authors

  • Antoine Bambade
  • Adrien Taylor
  • Fabian Schramm
  • Sarah El-Kazdadi
  • Justin Carpentier

Proxsuite Logo

License Documentation CI - Linux/OSX/Windows - Conda PyPI version Conda version

ProxSuite is a collection of open-source, numerically robust, precise, and efficient numerical solvers (e.g., LPs, QPs, etc.) rooted in revisited primal-dual proximal algorithms. Through ProxSuite, we aim to offer the community scalable optimizers that deal with dense, sparse, or matrix-free problems. While the first targeted application is Robotics, ProxSuite can be used in other contexts without limits.

ProxSuite is actively developped and supported by the Willow and Sierra research groups, joint research teams between Inria, École Normale Supérieure de Paris and Centre National de la Recherche Scientifique localized in France.

ProxSuite is already integrated into: - CVXPY modeling language for convex optimization problems, - CasADi's symbolic framework for numerical optimization in general and optimal control. ProxQP is available in CasADi as a plugin to solve quadratic programs, - TSID: robotic software for efficient robot inverse dynamics with contacts and based on Pinocchio.

We are ready to integrate ProxSuite within other optimization ecosystems.

ProxSuite main features

Proxsuite is fast:

  • C++ template library,
  • cache-friendly.

Proxsuite is versatile, offering through a unified API advanced algorithms specialized for efficiently exploiting problem structures:

  • dense, sparse, and matrix-free matrix factorization backends,
  • advanced warm-starting options (e.g., equality-constrained initial guess, warm-start or cold-start options from previous results),

with dedicated features for - handling more efficiently box constraints, linear programs, QP with diagonal Hessian, or with far more constraints than primal variables, - solving nonconvex QPs, - solving batches of QPs in parallel, - solving the closest feasible QP if the QP appears to be primal infeasible, - differentiating feasible and infeasible QPs.

Proxsuite is flexible:

  • header only,
  • C++ 14/17/20 compliant,
  • Python and Julia bindings for easy code prototyping without sacrificing performance.

Proxsuite is extensible. Proxsuite is reliable and extensively tested, showing the best performances on the hardest problems of the literature. Proxsuite is supported and tested on Windows, Mac OS X, Unix, and Linux.

Documentation

The online ProxSuite documentation of the last release is available here.

Getting started

ProxSuite is distributed to many well-known package managers.

Quick install with :

   pip install proxsuite

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   conda install proxsuite -c conda-forge

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   brew install proxsuite

This approach is available on Linux and Mac OS X.

Alternative approaches

Installation from source is presented here.

Compiling a first example program

For the fastest performance, use the following command to enable vectorization when compiling the simple example.

g++ -O3 -march=native -DNDEBUG -std=gnu++17 -DPROXSUITE_VECTORIZE examples/first_example_dense.cpp -o first_example_dense $(pkg-config --cflags proxsuite)

Using ProxSuite with CMake

If you want to use ProxSuite with CMake, the following tiny example should help you:

cmake_minimum_required(VERSION 3.10)

project(Example CXX)
find_package(proxsuite REQUIRED)
set(CMAKE_CXX_STANDARD 17) # set(CMAKE_CXX_STANDARD 14) will work too

add_executable(example example.cpp)
target_link_libraries(example PUBLIC proxsuite::proxsuite)

# Vectorization support via SIMDE and activated by the compilation options '-march=native' or `-mavx2 -mavx512f`
add_executable(example_with_full_vectorization_support example.cpp)
target_link_libraries(example_with_full_vectorization_support PUBLIC proxsuite::proxsuite-vectorized)
target_compile_options(example_with_full_vectorization_support PUBLIC "-march=native")

If you have compiled ProxSuite with the vectorization support, you might also use the CMake target proxsuite::proxsuite-vectorized to also link against SIMDE. Don't forget to use -march=native to get the best performance.

ProxQP

The ProxQP algorithm is a numerical optimization approach for solving quadratic programming problems of the form:

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}Hx+g^{T}x \ \text{s.t.} & ~A x = b \ & ~l \leq C x \leq u \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H \in \mathcal{S}^n_+$ and a vector $g \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l \in \mathbb{R}^{n_\text{in}}$ and $u \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

Citing ProxQP

If you are using ProxQP for your work, we encourage you to cite the related paper.

Numerical benchmarks

The numerical benchmarks of ProxQP against other commercial and open-source solvers are available here.

For dense Convex Quadratic Programs with inequality and equality constraints, when asking for relatively high accuracy (e.g., 1e-6), one obtains the following results.

Random Mixed QP_dense_eps_abs_1e-6

On the y-axis, you can see timings in seconds, and on the x-axis dimension wrt to the primal variable of the random Quadratic problems generated (the number of constraints of the generated problem is half the size of its primal dimension). For every dimension, the problem is generated over different seeds, and timings are obtained as averages over successive runs for the same problems. This chart shows for every benchmarked solver and random Quadratic program generated, barplot timings, including median (as a dot) and minimal and maximal values obtained (defining the amplitude of the bar). You can see that ProxQP is always below over solvers, which means it is the quickest for this test.

For hard problems from the Maros Meszaros testset, when asking for high accuracy (e.g., 1e-9), one obtains the results below.

maros_meszaros_problems_high_accuracy

The chart above reports the performance profiles of different solvers. It is classic for benchmarking solvers. Performance profiles correspond to the fraction of problems solved (on the y-axis) as a function of certain runtime (on the x-axis, measured in terms of a multiple of the runtime of the fastest solver for that problem). So the higher, the better. You can see that ProxQP solves the quickest over 60% of the problems (i.e., for $\tau=1$) and that for solving about 90% of the problems, it is at most 2 times slower than the fastest solvers solving these problems (i.e., for $\tau\approx2$).

Note: All these results have been obtained with a 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz CPU.

QPLayer

QPLayer enables to use a QP as a layer within standard learning architectures. More precisely, QPLayer differentiates over $\theta$ the primal and dual solutions of QP of the form

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}H(\theta)x+g(\theta)^{T}x \ \text{s.t.} & ~A(\theta) x = b(\theta) \ & ~l(\theta) \leq C(\theta) x \leq u(\theta) \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H(\theta) \in \mathcal{S}^n_+$ and a vector $g(\theta) \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A(\theta) \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C(\theta) \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l(\theta) \in \mathbb{R}^{n_\text{in}}$ and $u(\theta) \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

QPLayer is able to learn more structured architectures. For example, $\theta$ can consists only in learning some elements of $A$ while letting $b$ fixed (see e.g., the example about how to include QPLayer into a learning pipeline). QPLayer can also differentiates over LPs. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch.

Citing QPLayer

If you are using QPLayer for your work, we encourage you to cite the related paper.

Installation procedure

Please follow the installation procedure here.

CHANGELOG

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

Unreleased

What's Changed

  • Changed primal_infeasibility_solving to False for feasible QPs (#302)

0.6.3 - 2024-01-23

Fixed

  • Fix Python tests with scipy>=1.12 (#299)

0.6.2 - 2024-01-22

Fixed

  • Fix Windows build (#290)
  • Fix math formulae in documentation (#294)
  • Restore correc values for infeasibility (#292)
  • Handles CPU/GPU transfer in QPFunctionFn's backward function (#297)

0.6.1 - 2023-11-16

What's Changed

New Contributors

0.6.0 - 2023-11-13

News

We add the implementation of QPLayer. QPLayer enables to use a QP as a layer within standard learning architectures. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch. QPLayer can also differentiate over LPs.

What's Changed

0.5.1 - 2023-11-09

What's Changed

0.5.0 - 2023-09-26

This release adds support for nonconvex QPs, along with healthy fixes.

What's Changed

New Contributors

0.4.1 - 2023-08-02

What's Changed

0.4.0 - 2023-07-10

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#225)
  • add generalized primal dual augmented Lagrangian (gpdal) for dense backend by @Bambade (#228)
  • optimize dense iterative refinement by @Bambade (#230)
  • Add dense LP interface by @Bambade (#231)
  • Enable solving QP (#229)
  • Add small dense LP Python example by @stephane-caron (#235)
  • Fix typo (#234)
  • ci check all jobs pass by @fabinsch (#236)
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#232)
  • Add box constraint interface for dense backend by @Bambade (#238)
  • Improve dense backend and simplify calculus when using a Diagonal Hessian by @Bambade (#239)
  • Add infeasibility solving feature for dense and sparse backends by @Bambade (#241)
  • cmake: fix path to find-external/OpenMP by @fabinsch (#240)
  • More information (#242)
  • Fix warning and clean // solvers API by @jcarpent (#243)

0.3.7 - 2023-05-05

What's Changed

New Contributors

0.3.6 - 2023-03-14

What's Changed

0.3.5 - 2023-03-06

What's Changed

0.3.4 - 2023-03-01

What's Changed

  • CI: pip wheels on windows by @fabinsch (#185)
  • [CI] ROS: Add friendly names; add TSID on Rolling as downstream test by @wxmerkt (#184)
  • CI: simplify workflow on self-hosted M1 by @fabinsch (#187)

[0.3.3] - 2023-02-25

What's Changed

New Contributors

0.3.2 - 2023-01-17

What's Changed

New Contributors

0.3.1 - 2023-01-09

What's Changed

New Contributors

0.3.0 - 2022-12-26

What's Changed

0.2.16 - 2022-12-21

What's Changed

0.2.15 - 2022-12-15

What's Changed

0.2.14 - 2022-12-14

What's Changed

0.2.13 - 2022-11-29

What's Changed

0.2.12 - 2022-11-26

What's Changed

New Contributors

0.2.11 - 2022-11-25

What's Changed

0.2.10 - 2022-11-17

What's Changed

0.2.9 - 2022-11-14

What's Changed

  • Enforce robustness computation of duality gap by @jcarpent (#114)
  • Assert primal/dual residual and dual gap != nan + add unittest by @fabinsch (#115)

0.2.8 - 2022-11-12

What's Changed

0.2.7 - 2022-11-10

What's Changed

  • CMakeLists: fix INTERFACE target compile definitions by @fabinsch (#97)
  • sync submodule by @fabinsch (#98)
  • Fixed temporaries (#100)
  • Additional option to check eigen runtime malloc by @fabinsch (#93)

New Contributors

0.2.6 - 2022-11-08

What's Changed

0.2.5 - 2022-11-06

What's Changed

  • C++14 compliant implementation of optional by @fabinsch (#78)
  • C++14 compliant implementation of aligned_alloc by @fabinsch (#79)
  • unittest/sparse-ruiz: replace checks with isApprox by @fabinsch (#83)
  • Fix packaging for pip by @jcarpent (#82)
  • Move optional.hpp to the right place by @jcarpent (#81)
  • Fix logic and bug (#85)

0.2.4 - 2022-11-01

What's Changed

[0.2.3] - 2022-10-29

What's Changed

0.2.2 - 2022-10-19

What's Changed

  • release: wheels for macos arm64 by @fabinsch (#53)
  • Don't compile AVX Python bindings on non X86_64 arch by @jcarpent (#54)
  • Fix existence of std::aligned_alloc on APPLE by @jcarpent (#55)

0.2.1 - 2022-10-18

What's Changed

0.2.0 - 2022-10-08

This release introduces a notable change (#) As the API is not yet totally fixed, we have only increased the minor release version.

More to come (#)

What's Changed

New Contributors

0.1.2 - 2022-09-26

What's Changed

0.1.1 - 2022-09-09

What's Changed

New Contributors

0.1.0 - 2022-08-24

What's Changed

  • Fix aligned alloc for old version of OSX target deployment by @jcarpent (#3)
  • Fix documentation and the publishing pipeline by @jcarpent (#4)
  • Bindings: expose qp solver output by @fabinsch (#11)
  • provide initialization of solvers with None entries by @Bambade (#12)
  • Fix packaging issues and add more packaging test by @jcarpent (#17)
  • Use PROXSUITE_VECTORIZE and change logic by @jcarpent (#18)

New Contributors

[0.0.1] - 2022-08-11

The first release of ProxSuite.

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Package Dependencies

Deps Name
1 catkin

System Dependencies

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged proxsuite at Robotics Stack Exchange

proxsuite package from proxsuite repo

proxsuite

Package Summary

Tags No category tags.
Version 0.6.3
License BSD-2
Build type CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/Simple-Robotics/proxsuite.git
VCS Type git
VCS Version devel
Last Updated 2024-02-13
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

The Advanced Proximal Optimization Toolbox

Additional Links

Maintainers

  • Justin Carpentier
  • Wolfgang Merkt

Authors

  • Antoine Bambade
  • Adrien Taylor
  • Fabian Schramm
  • Sarah El-Kazdadi
  • Justin Carpentier

Proxsuite Logo

License Documentation CI - Linux/OSX/Windows - Conda PyPI version Conda version

ProxSuite is a collection of open-source, numerically robust, precise, and efficient numerical solvers (e.g., LPs, QPs, etc.) rooted in revisited primal-dual proximal algorithms. Through ProxSuite, we aim to offer the community scalable optimizers that deal with dense, sparse, or matrix-free problems. While the first targeted application is Robotics, ProxSuite can be used in other contexts without limits.

ProxSuite is actively developped and supported by the Willow and Sierra research groups, joint research teams between Inria, École Normale Supérieure de Paris and Centre National de la Recherche Scientifique localized in France.

ProxSuite is already integrated into: - CVXPY modeling language for convex optimization problems, - CasADi's symbolic framework for numerical optimization in general and optimal control. ProxQP is available in CasADi as a plugin to solve quadratic programs, - TSID: robotic software for efficient robot inverse dynamics with contacts and based on Pinocchio.

We are ready to integrate ProxSuite within other optimization ecosystems.

ProxSuite main features

Proxsuite is fast:

  • C++ template library,
  • cache-friendly.

Proxsuite is versatile, offering through a unified API advanced algorithms specialized for efficiently exploiting problem structures:

  • dense, sparse, and matrix-free matrix factorization backends,
  • advanced warm-starting options (e.g., equality-constrained initial guess, warm-start or cold-start options from previous results),

with dedicated features for - handling more efficiently box constraints, linear programs, QP with diagonal Hessian, or with far more constraints than primal variables, - solving nonconvex QPs, - solving batches of QPs in parallel, - solving the closest feasible QP if the QP appears to be primal infeasible, - differentiating feasible and infeasible QPs.

Proxsuite is flexible:

  • header only,
  • C++ 14/17/20 compliant,
  • Python and Julia bindings for easy code prototyping without sacrificing performance.

Proxsuite is extensible. Proxsuite is reliable and extensively tested, showing the best performances on the hardest problems of the literature. Proxsuite is supported and tested on Windows, Mac OS X, Unix, and Linux.

Documentation

The online ProxSuite documentation of the last release is available here.

Getting started

ProxSuite is distributed to many well-known package managers.

Quick install with :

   pip install proxsuite

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   conda install proxsuite -c conda-forge

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   brew install proxsuite

This approach is available on Linux and Mac OS X.

Alternative approaches

Installation from source is presented here.

Compiling a first example program

For the fastest performance, use the following command to enable vectorization when compiling the simple example.

g++ -O3 -march=native -DNDEBUG -std=gnu++17 -DPROXSUITE_VECTORIZE examples/first_example_dense.cpp -o first_example_dense $(pkg-config --cflags proxsuite)

Using ProxSuite with CMake

If you want to use ProxSuite with CMake, the following tiny example should help you:

cmake_minimum_required(VERSION 3.10)

project(Example CXX)
find_package(proxsuite REQUIRED)
set(CMAKE_CXX_STANDARD 17) # set(CMAKE_CXX_STANDARD 14) will work too

add_executable(example example.cpp)
target_link_libraries(example PUBLIC proxsuite::proxsuite)

# Vectorization support via SIMDE and activated by the compilation options '-march=native' or `-mavx2 -mavx512f`
add_executable(example_with_full_vectorization_support example.cpp)
target_link_libraries(example_with_full_vectorization_support PUBLIC proxsuite::proxsuite-vectorized)
target_compile_options(example_with_full_vectorization_support PUBLIC "-march=native")

If you have compiled ProxSuite with the vectorization support, you might also use the CMake target proxsuite::proxsuite-vectorized to also link against SIMDE. Don't forget to use -march=native to get the best performance.

ProxQP

The ProxQP algorithm is a numerical optimization approach for solving quadratic programming problems of the form:

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}Hx+g^{T}x \ \text{s.t.} & ~A x = b \ & ~l \leq C x \leq u \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H \in \mathcal{S}^n_+$ and a vector $g \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l \in \mathbb{R}^{n_\text{in}}$ and $u \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

Citing ProxQP

If you are using ProxQP for your work, we encourage you to cite the related paper.

Numerical benchmarks

The numerical benchmarks of ProxQP against other commercial and open-source solvers are available here.

For dense Convex Quadratic Programs with inequality and equality constraints, when asking for relatively high accuracy (e.g., 1e-6), one obtains the following results.

Random Mixed QP_dense_eps_abs_1e-6

On the y-axis, you can see timings in seconds, and on the x-axis dimension wrt to the primal variable of the random Quadratic problems generated (the number of constraints of the generated problem is half the size of its primal dimension). For every dimension, the problem is generated over different seeds, and timings are obtained as averages over successive runs for the same problems. This chart shows for every benchmarked solver and random Quadratic program generated, barplot timings, including median (as a dot) and minimal and maximal values obtained (defining the amplitude of the bar). You can see that ProxQP is always below over solvers, which means it is the quickest for this test.

For hard problems from the Maros Meszaros testset, when asking for high accuracy (e.g., 1e-9), one obtains the results below.

maros_meszaros_problems_high_accuracy

The chart above reports the performance profiles of different solvers. It is classic for benchmarking solvers. Performance profiles correspond to the fraction of problems solved (on the y-axis) as a function of certain runtime (on the x-axis, measured in terms of a multiple of the runtime of the fastest solver for that problem). So the higher, the better. You can see that ProxQP solves the quickest over 60% of the problems (i.e., for $\tau=1$) and that for solving about 90% of the problems, it is at most 2 times slower than the fastest solvers solving these problems (i.e., for $\tau\approx2$).

Note: All these results have been obtained with a 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz CPU.

QPLayer

QPLayer enables to use a QP as a layer within standard learning architectures. More precisely, QPLayer differentiates over $\theta$ the primal and dual solutions of QP of the form

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}H(\theta)x+g(\theta)^{T}x \ \text{s.t.} & ~A(\theta) x = b(\theta) \ & ~l(\theta) \leq C(\theta) x \leq u(\theta) \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H(\theta) \in \mathcal{S}^n_+$ and a vector $g(\theta) \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A(\theta) \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C(\theta) \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l(\theta) \in \mathbb{R}^{n_\text{in}}$ and $u(\theta) \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

QPLayer is able to learn more structured architectures. For example, $\theta$ can consists only in learning some elements of $A$ while letting $b$ fixed (see e.g., the example about how to include QPLayer into a learning pipeline). QPLayer can also differentiates over LPs. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch.

Citing QPLayer

If you are using QPLayer for your work, we encourage you to cite the related paper.

Installation procedure

Please follow the installation procedure here.

CHANGELOG

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

Unreleased

What's Changed

  • Changed primal_infeasibility_solving to False for feasible QPs (#302)

0.6.3 - 2024-01-23

Fixed

  • Fix Python tests with scipy>=1.12 (#299)

0.6.2 - 2024-01-22

Fixed

  • Fix Windows build (#290)
  • Fix math formulae in documentation (#294)
  • Restore correc values for infeasibility (#292)
  • Handles CPU/GPU transfer in QPFunctionFn's backward function (#297)

0.6.1 - 2023-11-16

What's Changed

New Contributors

0.6.0 - 2023-11-13

News

We add the implementation of QPLayer. QPLayer enables to use a QP as a layer within standard learning architectures. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch. QPLayer can also differentiate over LPs.

What's Changed

0.5.1 - 2023-11-09

What's Changed

0.5.0 - 2023-09-26

This release adds support for nonconvex QPs, along with healthy fixes.

What's Changed

New Contributors

0.4.1 - 2023-08-02

What's Changed

0.4.0 - 2023-07-10

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#225)
  • add generalized primal dual augmented Lagrangian (gpdal) for dense backend by @Bambade (#228)
  • optimize dense iterative refinement by @Bambade (#230)
  • Add dense LP interface by @Bambade (#231)
  • Enable solving QP (#229)
  • Add small dense LP Python example by @stephane-caron (#235)
  • Fix typo (#234)
  • ci check all jobs pass by @fabinsch (#236)
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#232)
  • Add box constraint interface for dense backend by @Bambade (#238)
  • Improve dense backend and simplify calculus when using a Diagonal Hessian by @Bambade (#239)
  • Add infeasibility solving feature for dense and sparse backends by @Bambade (#241)
  • cmake: fix path to find-external/OpenMP by @fabinsch (#240)
  • More information (#242)
  • Fix warning and clean // solvers API by @jcarpent (#243)

0.3.7 - 2023-05-05

What's Changed

New Contributors

0.3.6 - 2023-03-14

What's Changed

0.3.5 - 2023-03-06

What's Changed

0.3.4 - 2023-03-01

What's Changed

  • CI: pip wheels on windows by @fabinsch (#185)
  • [CI] ROS: Add friendly names; add TSID on Rolling as downstream test by @wxmerkt (#184)
  • CI: simplify workflow on self-hosted M1 by @fabinsch (#187)

[0.3.3] - 2023-02-25

What's Changed

New Contributors

0.3.2 - 2023-01-17

What's Changed

New Contributors

0.3.1 - 2023-01-09

What's Changed

New Contributors

0.3.0 - 2022-12-26

What's Changed

0.2.16 - 2022-12-21

What's Changed

0.2.15 - 2022-12-15

What's Changed

0.2.14 - 2022-12-14

What's Changed

0.2.13 - 2022-11-29

What's Changed

0.2.12 - 2022-11-26

What's Changed

New Contributors

0.2.11 - 2022-11-25

What's Changed

0.2.10 - 2022-11-17

What's Changed

0.2.9 - 2022-11-14

What's Changed

  • Enforce robustness computation of duality gap by @jcarpent (#114)
  • Assert primal/dual residual and dual gap != nan + add unittest by @fabinsch (#115)

0.2.8 - 2022-11-12

What's Changed

0.2.7 - 2022-11-10

What's Changed

  • CMakeLists: fix INTERFACE target compile definitions by @fabinsch (#97)
  • sync submodule by @fabinsch (#98)
  • Fixed temporaries (#100)
  • Additional option to check eigen runtime malloc by @fabinsch (#93)

New Contributors

0.2.6 - 2022-11-08

What's Changed

0.2.5 - 2022-11-06

What's Changed

  • C++14 compliant implementation of optional by @fabinsch (#78)
  • C++14 compliant implementation of aligned_alloc by @fabinsch (#79)
  • unittest/sparse-ruiz: replace checks with isApprox by @fabinsch (#83)
  • Fix packaging for pip by @jcarpent (#82)
  • Move optional.hpp to the right place by @jcarpent (#81)
  • Fix logic and bug (#85)

0.2.4 - 2022-11-01

What's Changed

[0.2.3] - 2022-10-29

What's Changed

0.2.2 - 2022-10-19

What's Changed

  • release: wheels for macos arm64 by @fabinsch (#53)
  • Don't compile AVX Python bindings on non X86_64 arch by @jcarpent (#54)
  • Fix existence of std::aligned_alloc on APPLE by @jcarpent (#55)

0.2.1 - 2022-10-18

What's Changed

0.2.0 - 2022-10-08

This release introduces a notable change (#) As the API is not yet totally fixed, we have only increased the minor release version.

More to come (#)

What's Changed

New Contributors

0.1.2 - 2022-09-26

What's Changed

0.1.1 - 2022-09-09

What's Changed

New Contributors

0.1.0 - 2022-08-24

What's Changed

  • Fix aligned alloc for old version of OSX target deployment by @jcarpent (#3)
  • Fix documentation and the publishing pipeline by @jcarpent (#4)
  • Bindings: expose qp solver output by @fabinsch (#11)
  • provide initialization of solvers with None entries by @Bambade (#12)
  • Fix packaging issues and add more packaging test by @jcarpent (#17)
  • Use PROXSUITE_VECTORIZE and change logic by @jcarpent (#18)

New Contributors

[0.0.1] - 2022-08-11

The first release of ProxSuite.

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Package Dependencies

Deps Name
1 catkin

System Dependencies

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged proxsuite at Robotics Stack Exchange

proxsuite package from proxsuite repo

proxsuite

Package Summary

Tags No category tags.
Version 0.6.3
License BSD-2
Build type CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/Simple-Robotics/proxsuite.git
VCS Type git
VCS Version devel
Last Updated 2024-02-13
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

The Advanced Proximal Optimization Toolbox

Additional Links

Maintainers

  • Justin Carpentier
  • Wolfgang Merkt

Authors

  • Antoine Bambade
  • Adrien Taylor
  • Fabian Schramm
  • Sarah El-Kazdadi
  • Justin Carpentier

Proxsuite Logo

License Documentation CI - Linux/OSX/Windows - Conda PyPI version Conda version

ProxSuite is a collection of open-source, numerically robust, precise, and efficient numerical solvers (e.g., LPs, QPs, etc.) rooted in revisited primal-dual proximal algorithms. Through ProxSuite, we aim to offer the community scalable optimizers that deal with dense, sparse, or matrix-free problems. While the first targeted application is Robotics, ProxSuite can be used in other contexts without limits.

ProxSuite is actively developped and supported by the Willow and Sierra research groups, joint research teams between Inria, École Normale Supérieure de Paris and Centre National de la Recherche Scientifique localized in France.

ProxSuite is already integrated into: - CVXPY modeling language for convex optimization problems, - CasADi's symbolic framework for numerical optimization in general and optimal control. ProxQP is available in CasADi as a plugin to solve quadratic programs, - TSID: robotic software for efficient robot inverse dynamics with contacts and based on Pinocchio.

We are ready to integrate ProxSuite within other optimization ecosystems.

ProxSuite main features

Proxsuite is fast:

  • C++ template library,
  • cache-friendly.

Proxsuite is versatile, offering through a unified API advanced algorithms specialized for efficiently exploiting problem structures:

  • dense, sparse, and matrix-free matrix factorization backends,
  • advanced warm-starting options (e.g., equality-constrained initial guess, warm-start or cold-start options from previous results),

with dedicated features for - handling more efficiently box constraints, linear programs, QP with diagonal Hessian, or with far more constraints than primal variables, - solving nonconvex QPs, - solving batches of QPs in parallel, - solving the closest feasible QP if the QP appears to be primal infeasible, - differentiating feasible and infeasible QPs.

Proxsuite is flexible:

  • header only,
  • C++ 14/17/20 compliant,
  • Python and Julia bindings for easy code prototyping without sacrificing performance.

Proxsuite is extensible. Proxsuite is reliable and extensively tested, showing the best performances on the hardest problems of the literature. Proxsuite is supported and tested on Windows, Mac OS X, Unix, and Linux.

Documentation

The online ProxSuite documentation of the last release is available here.

Getting started

ProxSuite is distributed to many well-known package managers.

Quick install with :

   pip install proxsuite

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   conda install proxsuite -c conda-forge

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   brew install proxsuite

This approach is available on Linux and Mac OS X.

Alternative approaches

Installation from source is presented here.

Compiling a first example program

For the fastest performance, use the following command to enable vectorization when compiling the simple example.

g++ -O3 -march=native -DNDEBUG -std=gnu++17 -DPROXSUITE_VECTORIZE examples/first_example_dense.cpp -o first_example_dense $(pkg-config --cflags proxsuite)

Using ProxSuite with CMake

If you want to use ProxSuite with CMake, the following tiny example should help you:

cmake_minimum_required(VERSION 3.10)

project(Example CXX)
find_package(proxsuite REQUIRED)
set(CMAKE_CXX_STANDARD 17) # set(CMAKE_CXX_STANDARD 14) will work too

add_executable(example example.cpp)
target_link_libraries(example PUBLIC proxsuite::proxsuite)

# Vectorization support via SIMDE and activated by the compilation options '-march=native' or `-mavx2 -mavx512f`
add_executable(example_with_full_vectorization_support example.cpp)
target_link_libraries(example_with_full_vectorization_support PUBLIC proxsuite::proxsuite-vectorized)
target_compile_options(example_with_full_vectorization_support PUBLIC "-march=native")

If you have compiled ProxSuite with the vectorization support, you might also use the CMake target proxsuite::proxsuite-vectorized to also link against SIMDE. Don't forget to use -march=native to get the best performance.

ProxQP

The ProxQP algorithm is a numerical optimization approach for solving quadratic programming problems of the form:

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}Hx+g^{T}x \ \text{s.t.} & ~A x = b \ & ~l \leq C x \leq u \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H \in \mathcal{S}^n_+$ and a vector $g \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l \in \mathbb{R}^{n_\text{in}}$ and $u \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

Citing ProxQP

If you are using ProxQP for your work, we encourage you to cite the related paper.

Numerical benchmarks

The numerical benchmarks of ProxQP against other commercial and open-source solvers are available here.

For dense Convex Quadratic Programs with inequality and equality constraints, when asking for relatively high accuracy (e.g., 1e-6), one obtains the following results.

Random Mixed QP_dense_eps_abs_1e-6

On the y-axis, you can see timings in seconds, and on the x-axis dimension wrt to the primal variable of the random Quadratic problems generated (the number of constraints of the generated problem is half the size of its primal dimension). For every dimension, the problem is generated over different seeds, and timings are obtained as averages over successive runs for the same problems. This chart shows for every benchmarked solver and random Quadratic program generated, barplot timings, including median (as a dot) and minimal and maximal values obtained (defining the amplitude of the bar). You can see that ProxQP is always below over solvers, which means it is the quickest for this test.

For hard problems from the Maros Meszaros testset, when asking for high accuracy (e.g., 1e-9), one obtains the results below.

maros_meszaros_problems_high_accuracy

The chart above reports the performance profiles of different solvers. It is classic for benchmarking solvers. Performance profiles correspond to the fraction of problems solved (on the y-axis) as a function of certain runtime (on the x-axis, measured in terms of a multiple of the runtime of the fastest solver for that problem). So the higher, the better. You can see that ProxQP solves the quickest over 60% of the problems (i.e., for $\tau=1$) and that for solving about 90% of the problems, it is at most 2 times slower than the fastest solvers solving these problems (i.e., for $\tau\approx2$).

Note: All these results have been obtained with a 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz CPU.

QPLayer

QPLayer enables to use a QP as a layer within standard learning architectures. More precisely, QPLayer differentiates over $\theta$ the primal and dual solutions of QP of the form

$$ \begin{align} \min_{x} & ~\frac{1}{2}x^{T}H(\theta)x+g(\theta)^{T}x \ \text{s.t.} & ~A(\theta) x = b(\theta) \ & ~l(\theta) \leq C(\theta) x \leq u(\theta) \end{align} $$

where $x \in \mathbb{R}^n$ is the optimization variable. The objective function is defined by a positive semidefinite matrix $H(\theta) \in \mathcal{S}^n_+$ and a vector $g(\theta) \in \mathbb{R}^n$. The linear constraints are defined by the equality-contraint matrix $A(\theta) \in \mathbb{R}^{n_\text{eq} \times n}$ and the inequality-constraint matrix $C(\theta) \in \mathbb{R}^{n_\text{in} \times n}$ and the vectors $b \in \mathbb{R}^{n_\text{eq}}$, $l(\theta) \in \mathbb{R}^{n_\text{in}}$ and $u(\theta) \in \mathbb{R}^{n_\text{in}}$ so that $b_i \in \mathbb{R},~ \forall i = 1,...,n_\text{eq}$ and $l_i \in \mathbb{R} \cup { -\infty }$ and $u_i \in \mathbb{R} \cup { +\infty }, ~\forall i = 1,...,n_\text{in}$.

QPLayer is able to learn more structured architectures. For example, $\theta$ can consists only in learning some elements of $A$ while letting $b$ fixed (see e.g., the example about how to include QPLayer into a learning pipeline). QPLayer can also differentiates over LPs. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch.

Citing QPLayer

If you are using QPLayer for your work, we encourage you to cite the related paper.

Installation procedure

Please follow the installation procedure here.

CHANGELOG

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

Unreleased

What's Changed

  • Changed primal_infeasibility_solving to False for feasible QPs (#302)

0.6.3 - 2024-01-23

Fixed

  • Fix Python tests with scipy>=1.12 (#299)

0.6.2 - 2024-01-22

Fixed

  • Fix Windows build (#290)
  • Fix math formulae in documentation (#294)
  • Restore correc values for infeasibility (#292)
  • Handles CPU/GPU transfer in QPFunctionFn's backward function (#297)

0.6.1 - 2023-11-16

What's Changed

New Contributors

0.6.0 - 2023-11-13

News

We add the implementation of QPLayer. QPLayer enables to use a QP as a layer within standard learning architectures. QPLayer allows for parallelized calculus over CPUs, and is interfaced with PyTorch. QPLayer can also differentiate over LPs.

What's Changed

0.5.1 - 2023-11-09

What's Changed

0.5.0 - 2023-09-26

This release adds support for nonconvex QPs, along with healthy fixes.

What's Changed

New Contributors

0.4.1 - 2023-08-02

What's Changed

0.4.0 - 2023-07-10

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#225)
  • add generalized primal dual augmented Lagrangian (gpdal) for dense backend by @Bambade (#228)
  • optimize dense iterative refinement by @Bambade (#230)
  • Add dense LP interface by @Bambade (#231)
  • Enable solving QP (#229)
  • Add small dense LP Python example by @stephane-caron (#235)
  • Fix typo (#234)
  • ci check all jobs pass by @fabinsch (#236)
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci (#232)
  • Add box constraint interface for dense backend by @Bambade (#238)
  • Improve dense backend and simplify calculus when using a Diagonal Hessian by @Bambade (#239)
  • Add infeasibility solving feature for dense and sparse backends by @Bambade (#241)
  • cmake: fix path to find-external/OpenMP by @fabinsch (#240)
  • More information (#242)
  • Fix warning and clean // solvers API by @jcarpent (#243)

0.3.7 - 2023-05-05

What's Changed

New Contributors

0.3.6 - 2023-03-14

What's Changed

0.3.5 - 2023-03-06

What's Changed

0.3.4 - 2023-03-01

What's Changed

  • CI: pip wheels on windows by @fabinsch (#185)
  • [CI] ROS: Add friendly names; add TSID on Rolling as downstream test by @wxmerkt (#184)
  • CI: simplify workflow on self-hosted M1 by @fabinsch (#187)

[0.3.3] - 2023-02-25

What's Changed

New Contributors

0.3.2 - 2023-01-17

What's Changed

New Contributors

0.3.1 - 2023-01-09

What's Changed

New Contributors

0.3.0 - 2022-12-26

What's Changed

0.2.16 - 2022-12-21

What's Changed

0.2.15 - 2022-12-15

What's Changed

0.2.14 - 2022-12-14

What's Changed

0.2.13 - 2022-11-29

What's Changed

0.2.12 - 2022-11-26

What's Changed

New Contributors

0.2.11 - 2022-11-25

What's Changed

0.2.10 - 2022-11-17

What's Changed

0.2.9 - 2022-11-14

What's Changed

  • Enforce robustness computation of duality gap by @jcarpent (#114)
  • Assert primal/dual residual and dual gap != nan + add unittest by @fabinsch (#115)

0.2.8 - 2022-11-12

What's Changed

0.2.7 - 2022-11-10

What's Changed

  • CMakeLists: fix INTERFACE target compile definitions by @fabinsch (#97)
  • sync submodule by @fabinsch (#98)
  • Fixed temporaries (#100)
  • Additional option to check eigen runtime malloc by @fabinsch (#93)

New Contributors

0.2.6 - 2022-11-08

What's Changed

0.2.5 - 2022-11-06

What's Changed

  • C++14 compliant implementation of optional by @fabinsch (#78)
  • C++14 compliant implementation of aligned_alloc by @fabinsch (#79)
  • unittest/sparse-ruiz: replace checks with isApprox by @fabinsch (#83)
  • Fix packaging for pip by @jcarpent (#82)
  • Move optional.hpp to the right place by @jcarpent (#81)
  • Fix logic and bug (#85)

0.2.4 - 2022-11-01

What's Changed

[0.2.3] - 2022-10-29

What's Changed

0.2.2 - 2022-10-19

What's Changed

  • release: wheels for macos arm64 by @fabinsch (#53)
  • Don't compile AVX Python bindings on non X86_64 arch by @jcarpent (#54)
  • Fix existence of std::aligned_alloc on APPLE by @jcarpent (#55)

0.2.1 - 2022-10-18

What's Changed

0.2.0 - 2022-10-08

This release introduces a notable change (#) As the API is not yet totally fixed, we have only increased the minor release version.

More to come (#)

What's Changed

New Contributors

0.1.2 - 2022-09-26

What's Changed

0.1.1 - 2022-09-09

What's Changed

New Contributors

0.1.0 - 2022-08-24

What's Changed

  • Fix aligned alloc for old version of OSX target deployment by @jcarpent (#3)
  • Fix documentation and the publishing pipeline by @jcarpent (#4)
  • Bindings: expose qp solver output by @fabinsch (#11)
  • provide initialization of solvers with None entries by @Bambade (#12)
  • Fix packaging issues and add more packaging test by @jcarpent (#17)
  • Use PROXSUITE_VECTORIZE and change logic by @jcarpent (#18)

New Contributors

[0.0.1] - 2022-08-11

The first release of ProxSuite.

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Package Dependencies

Deps Name
1 catkin

System Dependencies

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged proxsuite at Robotics Stack Exchange

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