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Linear convergence of the generalized Douglas–Rachford algorithm for feasibility problems

Author

Listed:
  • Minh N. Dao

    (University of Newcastle)

  • Hung M. Phan

    (University of Massachusetts Lowell)

Abstract

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, we establish several local linear convergence results for the algorithm in solving feasibility problems with finitely many closed possibly nonconvex sets under different assumptions. Our findings not only relax some regularity conditions but also improve linear convergence rates in the literature. In the presence of convexity, the linear convergence is global.

Suggested Citation

  • Minh N. Dao & Hung M. Phan, 2018. "Linear convergence of the generalized Douglas–Rachford algorithm for feasibility problems," Journal of Global Optimization, Springer, vol. 72(3), pages 443-474, November.
    • Handle: RePEc:spr:jglopt:v:72:y:2018:i:3:d:10.1007_s10898-018-0654-x
    DOI: 10.1007/s10898-018-0654-x
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    References listed on IDEAS

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    1. Heinz H. Bauschke & Minh N. Dao & Dominikus Noll & Hung M. Phan, 2016. "On Slater’s condition and finite convergence of the Douglas–Rachford algorithm for solving convex feasibility problems in Euclidean spaces," Journal of Global Optimization, Springer, vol. 65(2), pages 329-349, June.
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    Cited by:

    1. Minh N. Dao & Matthew K. Tam, 2019. "A Lyapunov-type approach to convergence of the Douglas–Rachford algorithm for a nonconvex setting," Journal of Global Optimization, Springer, vol. 73(1), pages 83-112, January.
    2. Heinz H. Bauschke & Minh N. Dao & Scott B. Lindstrom, 2019. "The Douglas–Rachford algorithm for a hyperplane and a doubleton," Journal of Global Optimization, Springer, vol. 74(1), pages 79-93, May.
    3. Minh N. Dao & Neil D. Dizon & Jeffrey A. Hogan & Matthew K. Tam, 2021. "Constraint Reduction Reformulations for Projection Algorithms with Applications to Wavelet Construction," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 201-233, July.
    4. Ohad Giladi & Björn S. Rüffer, 2019. "A Lyapunov Function Construction for a Non-convex Douglas–Rachford Iteration," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 729-750, March.
    5. Minh N. Dao & Matthew K. Tam, 2019. "Union Averaged Operators with Applications to Proximal Algorithms for Min-Convex Functions," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 61-94, April.
    6. Francisco J. Aragón Artacho & Rubén Campoy & Matthew K. Tam, 2020. "The Douglas–Rachford algorithm for convex and nonconvex feasibility problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(2), pages 201-240, April.

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    2. Heinz H. Bauschke & Minh N. Dao & Scott B. Lindstrom, 2019. "The Douglas–Rachford algorithm for a hyperplane and a doubleton," Journal of Global Optimization, Springer, vol. 74(1), pages 79-93, May.
    3. Minh N. Dao & Matthew K. Tam, 2019. "A Lyapunov-type approach to convergence of the Douglas–Rachford algorithm for a nonconvex setting," Journal of Global Optimization, Springer, vol. 73(1), pages 83-112, January.

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