iPiasco: inertial proximal algorithm for strongly convex optimization. In this paper, we present a forward-backward splitting algorithm with additional inertial term for solving a strongly convex optimization problem of a certain type. The strongly convex objective function is assumed to be a sum of a non-smooth convex and a smooth convex function. This additional knowledge is used for deriving a worst-case convergence rate for the proposed algorithm. It is proved to be an optimal algorithm with linear rate of convergence. For certain problems this linear rate of convergence is better than the provably optimal worst-case rate of convergence for smooth strongly convex functions. We demonstrate the efficiency of the proposed algorithm in numerical experiments and examples from image processing.

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Kesornprom, Suparat; Cholamjiak, Prasit: Proximal type algorithms involving linesearch and inertial technique for split variational inclusion problem in Hilbert spaces with applications (2019)
  2. Ogbuisi, Ferdinard U.; Mewomo, Oluwatosin T.: Convergence analysis of an inertial accelerated iterative algorithm for solving split variational inequality problem (2019)
  3. Shehu, Yekini; Cholamjiak, Prasit: Iterative method with inertial for variational inequalities in Hilbert spaces (2019)
  4. Shehu, Yekini; Iyiola, Olaniyi S.; Li, Xiao-Huan; Dong, Qiao-Li: Convergence analysis of projection method for variational inequalities (2019)
  5. Stathopoulos, Giorgos; Jones, Colin N.: An inertial parallel and asynchronous forward-backward iteration for distributed convex optimization (2019)
  6. Thong, Duong Viet; Hieu, Dang Van: Inertial subgradient extragradient algorithms with line-search process for solving variational inequality problems and fixed point problems (2019)
  7. Wu, Zhongming; Li, Min: General inertial proximal gradient method for a class of nonconvex nonsmooth optimization problems (2019)
  8. Bednarczuk, E. M.; Jezierska, A.; Rutkowski, K. E.: Proximal primal-dual best approximation algorithm with memory (2018)
  9. Dong, Qiao-Li; Gibali, Aviv; Jiang, Dan; Ke, Shang-Hong: Convergence of projection and contraction algorithms with outer perturbations and their applications to sparse signals recovery (2018)
  10. Dong, Q. L.; Cho, Y. J.; Zhong, L. L.; Rassias, Th. M.: Inertial projection and contraction algorithms for variational inequalities (2018)
  11. Iyiola, Olaniyi. S.; Ogbuisi, Ferdinard U.; Shehu, Yekini: An inertial type iterative method with Armijo linesearch for nonmonotone equilibrium problems (2018)
  12. Quéau, Yvain; Durou, Jean-Denis; Aujol, Jean-François: Variational methods for normal integration (2018)
  13. Shehu, Yekini: Convergence rate analysis of inertial Krasnoselskii-Mann type iteration with applications (2018)
  14. Dong, Qiaoli; Jiang, Dan; Cholamjiak, Prasit; Shehu, Yekini: A strong convergence result involving an inertial forward-backward algorithm for monotone inclusions (2017)
  15. Dong, Q.-L.; Gibali, A.; Jiang, D.; Tang, Y.: Bounded perturbation resilience of extragradient-type methods and their applications (2017)
  16. Shehu, Yekini; Iyiola, Olaniyi S.: Convergence analysis for the proximal split feasibility problem using an inertial extrapolation term method (2017)
  17. Weickert, Joachim; Grewenig, Sven; Schroers, Christopher; Bruhn, Andrés: Cyclic schemes for PDE-based image analysis (2016)
  18. Chen, Caihua; Chan, Raymond H.; Ma, Shiqian; Yang, Junfeng: Inertial proximal ADMM for linearly constrained separable convex optimization (2015)
  19. Chen, Caihua; Ma, Shiqian; Yang, Junfeng: A general inertial proximal point algorithm for mixed variational inequality problem (2015)
  20. Kang, Myeongmin; Kang, Myungjoo; Jung, Miyoun: Inexact accelerated augmented Lagrangian methods (2015)

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