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相关论文: Douglas--Rachford is the best projection method

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Many iterative methods for solving optimization or feasibility problems have been invented, and often convergence of the iterates to some solution is proven. Under favourable conditions, one might have additional bounds on the distance of…

最优化与控制 · 数学 2020-04-14 Heinz H. Bauschke , Minh N. Dao , Dominikus Noll , Hung M. Phan

The Douglas-Rachford projection algorithm is an iterative method used to find a point in the intersection of closed constraint sets. The algorithm has been experimentally observed to solve various nonconvex feasibility problems which…

最优化与控制 · 数学 2020-04-06 Minh N. Dao , Matthew K. Tam

The Douglas-Rachford method, a projection algorithm designed to solve continuous optimization problems, forms the basis of a useful heuristic for solving combinatorial optimization problems. In order to successfully use the method, it is…

最优化与控制 · 数学 2019-04-22 Francisco J. Aragón Artacho , Rubén Campoy , Matthew K. Tam

We introduce and study a geometric modification of the Douglas-Rach\-ford method called the Circumcentered-Douglas-Rachford method. This method iterates by taking the intersection of bisectors of reflection steps for solving certain classes…

最优化与控制 · 数学 2020-08-11 Roger Behling , Jose Yunier Bello Cruz , Luiz-Rafael Santos

The Douglas--Rachford algorithm is a popular algorithm for solving both convex and nonconvex feasibility problems. While its behaviour is settled in the convex inconsistent case, the general nonconvex inconsistent case is far from being…

最优化与控制 · 数学 2020-04-14 Heinz H. Bauschke , Minh N. Dao , Scott B. Lindstrom

The Douglas-Rachford splitting method is a classical and widely used algorithm for solving monotone inclusions involving the sum of two maximally monotone operators. It was recently shown to be the unique frugal, no-lifting…

最优化与控制 · 数学 2025-12-12 Max Nilsson , Anton Åkerman , Pontus Giselsson

In this paper, we investigate the Douglas-Rachford method for two closed (possibly nonconvex) sets in Euclidean spaces. We show that under certain regularity conditions, the Douglas-Rachford method converges locally with R-linear rate. In…

最优化与控制 · 数学 2015-02-20 Hung M. Phan

The Douglas-Rachford splitting algorithm is a classical optimization method that has found many applications. When specialized to two normal cone operators, it yields an algorithm for finding a point in the intersection of two convex sets.…

最优化与控制 · 数学 2013-12-24 Heinz H. Bauschke , J. Y. Bello Cruz , Tran T. A. Nghia , Hung M. Phan , Xianfu Wang

In this paper, we study the generalized Douglas-Rachford algorithm and its cyclic variants which include many projection-type methods such as the classical Douglas-Rachford algorithm and the alternating projection algorithm. Specifically,…

最优化与控制 · 数学 2020-04-14 Minh N. Dao , Hung M. Phan

Solving feasibility problems is a central task in mathematics and the applied sciences. One particularly successful method is the Douglas-Rachford algorithm. In this paper, we provide many new conditions sufficient for finite convergence.…

最优化与控制 · 数学 2020-04-14 Heinz H. Bauschke , Minh N. Dao

The Douglas-Rachford algorithm is a classical and very successful method for solving optimization and feasibility problems. In this paper, we provide novel conditions sufficient for finite convergence in the context of convex feasibility…

最优化与控制 · 数学 2020-04-14 Heinz H. Bauschke , Minh N. Dao , Dominikus Noll , Hung M. Phan

We study the cyclic relaxed Douglas-Rachford algorithm for possibly nonconvex, and inconsistent feasibility problems. This algorithm can be viewed as a convex relaxation between the cyclic Douglas-Rachford algorithm first introduced by…

最优化与控制 · 数学 2026-05-06 Thi Lan Dinh , G. S. Matthijs Jansen , D. Russell Luke

In this paper we present two Douglas-Rachford inspired iteration schemes which can be applied directly to N-set convex feasibility problems in Hilbert space. Our main results are weak convergence of the methods to a point whose nearest…

最优化与控制 · 数学 2018-05-28 Jonathan M. Borwein , Matthew K. Tam

We discuss the Douglas-Rachford algorithm to solve the feasibility problem for two closed sets $A,B$ in $\mathbb{R}^d$. We prove its local convergence to a fixed point when $A,B$ are finite unions of convex sets. We also show that for more…

最优化与控制 · 数学 2014-01-27 H. H. Bauschke , D. Noll

This paper proposes an algorithm for solving structured optimization problems, which covers both the backward-backward and the Douglas-Rachford algorithms as special cases, and analyzes its convergence. The set of fixed points of the…

最优化与控制 · 数学 2017-09-19 Nguyen Hieu Thao

The Douglas-Rachford algorithm is a simple yet effective method for solving convex feasibility problems. However, if the underlying constraints are inconsistent, then the convergence theory is incomplete. We provide convergence results when…

最优化与控制 · 数学 2020-04-14 Heinz H. Bauschke , Minh N. Dao , Walaa M. Moursi

Feasibility problem aims to find a common point of two or more closed (convex) sets whose intersection is nonempty. In the literature, projection based algorithms are widely adopted to solve the problem, such as the method of alternating…

最优化与控制 · 数学 2025-04-16 Yuting Shen , Jingwei Liang

We present the convergence analysis of convex combination of the alternating projection and Douglas-Rachford operators for solving the phase retrieval problem. New convergence criteria for iterations generated by the algorithm are…

数值分析 · 数学 2020-02-06 Nguyen Hieu Thao , Oleg Soloviev , Michel Verhaegen

The Douglas--Rachford algorithm is a classical and very successful splitting method for finding the zeros of the sums of monotone operators. When the underlying operators are normal cone operators, the algorithm solves a convex feasibility…

最优化与控制 · 数学 2015-04-16 Heinz H. Bauschke , Walaa M. Moursi

In recent times the Douglas-Rachford algorithm has been observed empirically to solve a variety of nonconvex feasibility problems including those of a combinatorial nature. For many of these problems current theory is not sufficient to…

最优化与控制 · 数学 2017-07-24 Francisco J. Aragón Artacho , Jonathan M. Borwein , Matthew K. Tam
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