An inertial Tseng's type proximal algorithm for nonsmooth and nonconvex optimization problems

2014/06/03 by Bot, Radu Ioan, Csetnek, Ernö Robert · 2 citations
#65K10 #90C26 #90C30 #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1406.0724

Abstract

We investigate the convergence of a forward-backward-forward proximal-type algorithm with inertial and memory effects when minimizing the sum of a nonsmooth function with a smooth one in the absence of convexity. The convergence is obtained provided an appropriate regularization of the objective satisfies the Kurdyka-Łojasiewicz inequality, which is for instance fulfilled for semi-algebraic functions.

Cited by

• Relaxed and inertial nonlinear Forward-Backward algorithm
• General Proximal Quasi-Newton Methods based on model functions for nonsmooth nonconvex problems

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