Mathematics > Optimization and Control
[Submitted on 18 Oct 2022 (v1), last revised 26 Aug 2024 (this version, v5)]
Title:On convergence of a $q$-random coordinate constrained algorithm for non-convex problems
View PDF HTML (experimental)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.
Submission history
From: Lennart Sinjorgo [view email][v1] Tue, 18 Oct 2022 08:07:07 UTC (22 KB)
[v2] Wed, 19 Oct 2022 07:27:06 UTC (32 KB)
[v3] Thu, 7 Sep 2023 08:37:38 UTC (31 KB)
[v4] Tue, 19 Mar 2024 14:52:36 UTC (30 KB)
[v5] Mon, 26 Aug 2024 07:20:33 UTC (30 KB)
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