Convergence rate of a relaxed inertial proximal algorithm for convex minimization

DOI10.1080/02331934.2019.1696337zbMath1440.49014OpenAlexW2993347174MaRDI QIDQ5110325

Alexandre Cabot, Hedy Attouch

Publication date: 18 May 2020

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2019.1696337

zbMATH Keywords

relaxationLyapunov analysisnonsmooth convex minimizationmaximally monotone operatorsinertial proximal method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Numerical optimization and variational techniques (65K10) Numerical methods based on nonlinear programming (49M37) Methods involving semicontinuity and convergence; relaxation (49J45)

Related Items (10)

Relaxed inertial methods for solving split variational inequality problems without product space formulation ⋮ Weak and linear convergence of a generalized proximal point algorithm with alternating inertial steps for a monotone inclusion problem ⋮ Relaxed inertial Tseng extragradient method for variational inequality and fixed point problems ⋮ Unnamed Item ⋮ Smoothing accelerated proximal gradient method with fast convergence rate for nonsmooth convex optimization beyond differentiability ⋮ Relaxed-inertial proximal point type algorithms for quasiconvex minimization ⋮ Fast convex optimization via a third-order in time evolution equation: TOGES-V an improved version of TOGES* ⋮ Shrinking projection methods for accelerating relaxed inertial Tseng-type algorithm with applications ⋮ On convergence and complexity analysis of an accelerated forward-backward algorithm with linesearch technique for convex minimization problems and applications to data prediction and classification ⋮ Convergence results of two-step inertial proximal point algorithm

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