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

• Published in: Journal of global optimization Vol. 86; no. 3; pp. 621 - 636
• Main Authors: Díaz Millán, R., Ferreira, O. P., Ugon, J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2023; Springer; Springer Nature B.V
• Subjects: Algorithms; Approximation; Computer Science; Convergence; Feasibility; Iterative methods; Linear programming; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Real Functions; Shadows; Convex feasibility problem; Conditional gradient method; 65K05; 90C30; Frank–Wolfe algorithm; 90C25; Inexact projections; Douglas–Rachford algorithm
• ISSN: 0925-5001; 1573-2916
• DOI: 10.1007/s10898-022-01264-7

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.