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COSMO: A conic operator splitting method for convex conic problems

Abstract:
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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10957-021-01896-x

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St John's College
Role:
Author
ORCID:
0000-0003-2189-7876


Publisher:
Springer
Journal:
Journal of Optimization Theory and Applications More from this journal
Volume:
190
Issue:
3
Pages:
779-810
Publication date:
2021-08-29
Acceptance date:
2021-06-16
DOI:
EISSN:
1573-2878
ISSN:
0022-3239


Language:
English
Keywords:
Pubs id:
1207653
Local pid:
pubs:1207653
Deposit date:
2021-11-09

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