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Approximate Douglas–Rachford algorithm for two-sets convex feasibility problems

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Abstract

In this paper, we propose a new algorithm combining the Douglas–Rachford (DR) algorithm and the Frank–Wolfe algorithm, also known as the conditional gradient (CondG) method, for solving the classic convex feasibility problem. Within the algorithm, which will be named Approximate Douglas–Rachford (ApDR) algorithm, the CondG method is used as a subroutine to compute feasible inexact projections on the sets under consideration, and the ApDR iteration is defined based on the DR iteration. The ApDR algorithm generates two sequences, the main sequence, based on the DR iteration, and its corresponding shadow sequence. When the intersection of the feasible sets is nonempty, the main sequence converges to a fixed point of the usual DR operator, and the shadow sequence converges to the solution set. We provide some numerical experiments to illustrate the behaviour of the sequences produced by the proposed algorithm.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, Australia

    R. Díaz Millán & J. Ugon

  2. IME, Universidade Federal de Goiás, Goiânia, Brazil

    O. P. Ferreira

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  1. R. Díaz Millán
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  2. O. P. Ferreira
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  3. J. Ugon
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Correspondence to R. Díaz Millán.

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R. Díaz Millán: This author was supported by the Australian Research Council (ARC), Solving hard Chebyshev approximation problems through nonsmooth analysis (Discovery Project DP180100602).

O. P. Ferreira: This authors was supported in part by CNPq grant 304666/2021-1.

J. Ugon: This author was supported by the Australian Research Council (ARC), Solving hard Chebyshev approximation problems through nonsmooth analysis (Discovery Project DP180100602).

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Díaz Millán, R., Ferreira, O.P. & Ugon, J. Approximate Douglas–Rachford algorithm for two-sets convex feasibility problems. J Glob Optim 86, 621–636 (2023). https://doi.org/10.1007/s10898-022-01264-7

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