COSMO: A conic operator splitting method for convex conic problems

This paper describes the conic operator splitting method (COSMO) solver, an operator splitting algorithm and associated software package for convex optimisation problems with quadratic objective function and conic constraints. At each step, the algorithm alternates between solving a quasi-definite l...

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Main Authors: Garstka, M, Cannon, M, Goulart, P
Format: Journal article
Language:English
Published: Springer 2021
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author Garstka, M
Cannon, M
Goulart, P
author_facet Garstka, M
Cannon, M
Goulart, P
author_sort Garstka, M
collection OXFORD
description This paper describes the conic operator splitting method (COSMO) solver, an operator splitting algorithm and associated software package for convex optimisation problems with quadratic objective function and conic constraints. At each step, the algorithm alternates between solving a quasi-definite linear system with a constant coefficient matrix and a projection onto convex sets. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefinite programs that arise in portfolio optimisation, graph theory, and robust control. Moreover, the solver uses chordal decomposition techniques and a new clique merging algorithm to effectively exploit sparsity in large, structured semidefinite programs. Numerical comparisons with other state-of-the-art solvers for a variety of benchmark problems show the effectiveness of our approach. Our Julia implementation is open source, designed to be extended and customised by the user, and is integrated into the Julia optimisation ecosystem.
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spelling oxford-uuid:bd90358e-1778-4833-b848-4bcd86b4229f2022-03-27T05:32:43ZCOSMO: A conic operator splitting method for convex conic problemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:bd90358e-1778-4833-b848-4bcd86b4229fEnglishSymplectic ElementsSpringer2021Garstka, MCannon, MGoulart, PThis paper describes the conic operator splitting method (COSMO) solver, an operator splitting algorithm and associated software package for convex optimisation problems with quadratic objective function and conic constraints. At each step, the algorithm alternates between solving a quasi-definite linear system with a constant coefficient matrix and a projection onto convex sets. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefinite programs that arise in portfolio optimisation, graph theory, and robust control. Moreover, the solver uses chordal decomposition techniques and a new clique merging algorithm to effectively exploit sparsity in large, structured semidefinite programs. Numerical comparisons with other state-of-the-art solvers for a variety of benchmark problems show the effectiveness of our approach. Our Julia implementation is open source, designed to be extended and customised by the user, and is integrated into the Julia optimisation ecosystem.
spellingShingle Garstka, M
Cannon, M
Goulart, P
COSMO: A conic operator splitting method for convex conic problems
title COSMO: A conic operator splitting method for convex conic problems
title_full COSMO: A conic operator splitting method for convex conic problems
title_fullStr COSMO: A conic operator splitting method for convex conic problems
title_full_unstemmed COSMO: A conic operator splitting method for convex conic problems
title_short COSMO: A conic operator splitting method for convex conic problems
title_sort cosmo a conic operator splitting method for convex conic problems
work_keys_str_mv AT garstkam cosmoaconicoperatorsplittingmethodforconvexconicproblems
AT cannonm cosmoaconicoperatorsplittingmethodforconvexconicproblems
AT goulartp cosmoaconicoperatorsplittingmethodforconvexconicproblems