COSMO: A conic operator splitting method for large convex problems

This paper describes the Conic Operator Splitting Method (COSMO), an operator splitting algorithm 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...

Full description

Saved in:
Bibliographic Details
Published in2019 18th European Control Conference (ECC) pp. 1951 - 1956
Main Authors Garstka, Michael, Cannon, Mark, Goulart, Paul
Format Conference Proceeding
LanguageEnglish
Published EUCA 01.06.2019
Subjects
Ecosystems
Europe
Graph theory
Linear programming
Linear systems
Optimization
Parallel processing
Performance gain
Portfolios
Robust control
Online AccessGet full text
DOI10.23919/ECC.2019.8796161

Cover

More Information
Summary:This paper describes the Conic Operator Splitting Method (COSMO), an operator splitting algorithm 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 solver is able to exploit chordal sparsity in the problem data and to detect infeasible problems. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefinite programs in portfolio optimisation, graph theory, and robust control. Our Julia implementation is open-source, extensible, integrated into the Julia optimisation ecosystem and performs well on a variety of large convex problem classes.
DOI:10.23919/ECC.2019.8796161