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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).
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Versatile: COSMO solves linear programs, quadratic programs, second-order cone programs, semidefinite programs and problems involving exponential and power cones
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Quad SDPs: Positive semidefinite programs with quadratic objective functions are natively supported
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Infeasibility detection: Infeasible problems are detected without a homogeneous self-dual embedding of the problem
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JuMP / Convex.jl support: We provide an interface to MathOptInterface (MOI), which allows you to describe your problem in JuMP and Convex.jl.
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Warm starting: COSMO supports warm starting of the decision variables
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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
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Open Source: Our code is free to use and distributed under the Apache 2.0 Licence
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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.
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Smart clique merging: After an initial decomposition of a structured SDP, COSMO recombines overlapping cliques/blocks to speed up the algorithm.
COSMO
can be added via the Julia package manager (type]
):pkg> add COSMO
If you find COSMO useful in your project, we kindly request that you cite the following paper:
@InProceedings{garstka_2019,
author = {Michael Garstka and Mark Cannon and Paul Goulart},
title = {{COSMO}: A conic operator splitting method for large convex problems},
booktitle = {European Control Conference},
year = {2019},
location = {Naples, Italy},
doi = {10.23919/ECC.2019.8796161},
eprint = {1901.10887},
url = {https://arxiv.org/abs/1901.10887},
archiveprefix = {arXiv},
keywords = {Mathematics - Optimization and Control},
primaryclass = {math.OC},
}
A preprint can be downloaded here.
- 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!
This project is licensed under the Apache License - see the LICENSE.md file for details.