Fast Convergence of Greedy 2-Coordinate Updates for Optimizing with an Equality ConstraintDownload PDF

02 Oct 2022, 17:24 (modified: 28 Nov 2022, 17:13)OPT 2022 PosterReaders: Everyone
Abstract: We consider minimizing a smooth function subject to an equality constraint. We analyze a greedy 2-coordinate update algorithm, and prove that greedy coordinate selection leads to faster convergence than random selection (under the Polyak-\L{}ojasiewicz assumption). Our simple analysis exploits am equivalence between the greedy 2-coordinate update and equality-constrained steepest descent in the L1-norm. Unlike previous 2-coordinate analyses, our convergence rate is dimension independent.
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