Backward–forward algorithms for structured monotone inclusions in Hilbert spaces

In this paper, we study the backward–forward algorithm as a splitting method to solve structured monotone inclusions, and convex minimization problems in Hilbert spaces. It has a natural link with the forward–backward algorithm and has the same computational complexity, since it involves the same ba...

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Vydané v:Journal of mathematical analysis and applications Ročník 457; číslo 2; s. 1095 - 1117
Hlavní autori: Attouch, Hédy, Peypouquet, Juan, Redont, Patrick
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 15.01.2018
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ISSN:0022-247X, 1096-0813
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Abstract In this paper, we study the backward–forward algorithm as a splitting method to solve structured monotone inclusions, and convex minimization problems in Hilbert spaces. It has a natural link with the forward–backward algorithm and has the same computational complexity, since it involves the same basic blocks, but organized differently. Surprisingly enough, this kind of iteration arises when studying the time discretization of the regularized Newton method for maximally monotone operators. First, we show that these two methods enjoy remarkable involutive relations, which go far beyond the evident inversion of the order in which the forward and backward steps are applied. Next, we establish several convergence properties for both methods, some of which were unknown even for the forward–backward algorithm. This brings further insight into this well-known scheme. Finally, we specialize our results to structured convex minimization problems, the gradient-projection algorithms, and give a numerical illustration of theoretical interest.
AbstractList In this paper, we study the backward–forward algorithm as a splitting method to solve structured monotone inclusions, and convex minimization problems in Hilbert spaces. It has a natural link with the forward–backward algorithm and has the same computational complexity, since it involves the same basic blocks, but organized differently. Surprisingly enough, this kind of iteration arises when studying the time discretization of the regularized Newton method for maximally monotone operators. First, we show that these two methods enjoy remarkable involutive relations, which go far beyond the evident inversion of the order in which the forward and backward steps are applied. Next, we establish several convergence properties for both methods, some of which were unknown even for the forward–backward algorithm. This brings further insight into this well-known scheme. Finally, we specialize our results to structured convex minimization problems, the gradient-projection algorithms, and give a numerical illustration of theoretical interest.
Author Peypouquet, Juan
Redont, Patrick
Attouch, Hédy
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  email: patrick.redont@univ-montp2.fr
  organization: Institut Montpelliérain Alexander Grothendieck, IMAG UMR 5149 CNRS, Université Montpellier 2, place Eugène Bataillon, 34095 Montpellier Cedex 5, France
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Keywords Monotone inclusion
Proximal-gradient method
Forward–backward algorithm
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Snippet In this paper, we study the backward–forward algorithm as a splitting method to solve structured monotone inclusions, and convex minimization problems in...
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SubjectTerms Forward–backward algorithm
Monotone inclusion
Proximal-gradient method
Title Backward–forward algorithms for structured monotone inclusions in Hilbert spaces
URI https://dx.doi.org/10.1016/j.jmaa.2016.06.025
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