Circumcentering approximate reflections for solving the convex feasibility problem

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Circumcentering approximate reflections for solving the convex feasibility problem https://t.co/vO7D0sJgXB

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numerical experiments, we present a couple of illustrative examples. [5/5 of https://t.co/mQBemPse9o]

theoretical and numerical comparisons of CARM to the original CRM, to the classical method of alternating projections (MAP) and to a correspondent outer-approximate version of MAP, referred to as MAAP. Along with our results and [4/5 of https://t.co/mQBemP

that, in rather general situations, the approximate projections we employ are available under low computational cost. We derive convergence of CARM and linear convergence under an error bound condition. We also present successful [3/5 of https://t.co/mQBe

on exact orthogonal projections, their computation might be costly. In this regard, we introduce the circumcentered approximate-reflection method (CARM), whose reflections rely on outer-approximate projections. The appeal of CARM is [2/5 of https://t.co/mQ

The circumcentered-reflection method (CRM) has been applied for solving convex feasibility problems. CRM iterates by computing a circumcenter upon a composition of reflections with respect to convex sets. Since reflections are based [1/5 of https://t.co/mQ

Guilherme Araújo, Reza Arefidamghani, Roger Behling, Yunier Bello-Cruz, Alfredo Iusem, Luiz-Rafael Santos: Circumcentering approximate reflections for solving the convex feasibility problem https://t.co/sONuRbnhTk https://t.co/HpmplrYDcu