Convergence of an asynchronous block-coordinate forward-backward algorithm for convex composite optimization

Cheik Traoré (), Saverio Salzo () and Silvia Villa ()
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Cheik Traoré: Università degli Studi di Genova
Saverio Salzo: DIAG, Sapienza Università di Roma
Silvia Villa: Università degli Studi di Genova

Computational Optimization and Applications, 2023, vol. 86, issue 1, No 9, 303-344

Abstract: Abstract In this paper, we study the convergence properties of a randomized block-coordinate descent algorithm for the minimization of a composite convex objective function, where the block-coordinates are updated asynchronously and randomly according to an arbitrary probability distribution. We prove that the iterates generated by the algorithm form a stochastic quasi-Fejér sequence and thus converge almost surely to a minimizer of the objective function. Moreover, we prove a general sublinear rate of convergence in expectation for the function values and a linear rate of convergence in expectation under an error bound condition of Tseng type. Under the same condition strong convergence of the iterates is provided as well as their linear convergence rate.

Keywords: Convex optimization; Asynchronous algorithms; Randomized block-coordinate descent; Error bounds; Stochastic quasi-Fejér sequences; Forward-backward algorithm; Convergence rates; 65K05; 90C25; 90C06; 49M27 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10589-023-00489-w

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