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oxfordcontrol
182 Stars 27 Forks Apache License 2.0 688 Commits 20 Opened issues

Description

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.

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FeaturesInstallationNewsCitingContributing

This is a Julia implementation of the Conic operator splitting method (COSMO) solver. It can solve large convex conic optimization problems of the following form:

with decision variables

x ϵ R^n
,
s ϵ R^m
and data matrices
P=P'>=0
,
q ϵ R^n
,
A ϵ R^(m×n)
, and
b ϵ R^m
. The convex set
K
is a composition of convex sets and cones.

For more information take a look at the COSMO.jl Documentation (stable | dev).

Features

  • Versatile: COSMO solves linear programs, quadratic programs, second-order cone programs, semidefinite programs and problems involving exponential and power cones
  • Quad SDPs: Positive semidefinite programs with quadratic objective functions are natively supported
  • Safeguarded acceleration: robust and faster convergence to higher precision using COSMOAccelerators
  • Infeasibility detection: Infeasible problems are detected without a homogeneous self-dual embedding of the problem
  • JuMP / Convex.jl support: We provide an interface to MathOptInterface (MOI), which allows you to describe your problem in JuMP and Convex.jl.
  • Warm starting: COSMO supports warm starting of the decision variables
  • Custom sets and linear solver: Customize COSMO's components by defining your own convex constraint sets and by choosing from a number of direct and indirect linear system solvers, e.g. QDLDL, Pardiso, Conjugate Gradient and MINRES
  • Arbitrary precision types: You can solve problems with any floating point precision.
  • Open Source: Our code is free to use and distributed under the Apache 2.0 Licence
  • Chordal decomposition: COSMO tries to decompose large structured PSD constraints using chordal decomposition techniques. This often results in a significant speedup compared to the original problem.
  • Smart clique merging: After an initial decomposition of a structured SDP, COSMO recombines overlapping cliques/blocks to speed up the algorithm.

Installation

  • COSMO
    can be added via the Julia package manager (type
    ]
    ):
    pkg> add COSMO

Citing

If you find COSMO useful in your project, we kindly request that you cite the following paper:

@Article{Garstka_2021,
  author  = {Michael Garstka and Mark Cannon and Paul Goulart},
  journal = {Journal of Optimization Theory and Applications},
  title   = {{COSMO}: A Conic Operator Splitting Method for Convex Conic Problems},
  year    = {2021},
  doi     = {10.1007/s10957-021-01896-x},
  url     = {https://doi.org/10.1007/s10957-021-01896-x},
}
The article is available under Open Access here.

Contributing

  • Contributions are always welcome. Our style guide can be found here.
  • Current issues, tasks and future ideas are listed in Issues. Please report any issues or bugs that you encounter.
  • As an open source project we are also interested in any projects and applications that use COSMO. Please let us know!

Python - Interface

COSMO can also be called from Python. Take a look at: cosmo-python

Licence 🔍

This project is licensed under the Apache License - see the LICENSE.md file for details.

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