On convergence of a q-random coordinate constrained algorithm for non-convex problems

A. Ghaffari-Hadigheh (), L. Sinjorgo () and R. Sotirov ()
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A. Ghaffari-Hadigheh: Azarbaijan Shahid Madani University
L. Sinjorgo: Tilburg University
R. Sotirov: Tilburg University

Journal of Global Optimization, 2024, vol. 90, issue 4, No 2, 843-868

Abstract: Abstract We propose a random coordinate descent algorithm for optimizing a non-convex objective function subject to one linear constraint and simple bounds on the variables. Although it is common use to update only two random coordinates simultaneously in each iteration of a coordinate descent algorithm, our algorithm allows updating arbitrary number of coordinates. We provide a proof of convergence of the algorithm. The convergence rate of the algorithm improves when we update more coordinates per iteration. Numerical experiments on large scale instances of different optimization problems show the benefit of updating many coordinates simultaneously.

Keywords: Random coordinate descent algorithm; Convergence analysis; Densest k-subgraph problem; Eigenvalue complementarity problem; 90C06; 90C30; 90C26 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10898-024-01429-6

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