Convergence Properties of Monotone and Nonmonotone Proximal Gradient Methods Revisited

Christian Kanzow () and Patrick Mehlitz ()
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Christian Kanzow: University of Würzburg
Patrick Mehlitz: Brandenburg University of Technology Cottbus-Senftenberg

Journal of Optimization Theory and Applications, 2022, vol. 195, issue 2, No 10, 624-646

Abstract: Abstract Composite optimization problems, where the sum of a smooth and a merely lower semicontinuous function has to be minimized, are often tackled numerically by means of proximal gradient methods as soon as the lower semicontinuous part of the objective function is of simple enough structure. The available convergence theory associated with these methods (mostly) requires the derivative of the smooth part of the objective function to be (globally) Lipschitz continuous, and this might be a restrictive assumption in some practically relevant scenarios. In this paper, we readdress this classical topic and provide convergence results for the classical (monotone) proximal gradient method and one of its nonmonotone extensions which are applicable in the absence of (strong) Lipschitz assumptions. This is possible since, for the price of forgoing convergence rates, we omit the use of descent-type lemmas in our analysis.

Keywords: Non-Lipschitz optimization; Nonsmooth optimization; Proximal gradient method; 49J52; 90C30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10957-022-02101-3

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