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On the local convergence of the semismooth Newton method for composite optimization
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In this paper, we consider a large class of nonlinear equations derived from first-order type methods for solving composite optimization problems. Traditional approaches to establishing superlinear convergence rates of semismooth Newton-type methods for solving nonlinear equations usually postulate either nonsingularity of the B-Jacobian or smoothness of the equation. We investigate the feasibility of both conditions. For the nonsingularity condition, we present equivalent characterizations in broad generality, and illustrate that they are easy-to-check criteria for some examples. For the smoothness condition, we show that it holds locally for a large class of residual mappings derived from composite optimization problems. Furthermore, we investigate a relaxed version of the smoothness condition - smoothness restricted to certain active manifolds. We present a conceptual algorithm utilizing such structures and prove that it has a superlinear convergence rate.
Forward citations
Cited by 2 Pith papers
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Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework
A new pathwise Lyapunov-Perron framework proves almost sure saddle avoidance for stochastic recursions without unit excitation, covering SGD, mirror descent, proximal stochastic gradient, and random reshuffling.
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A Globalized Semismooth Newton Method for Prox-regular Optimization Problems
A hybrid proximal-gradient/semismooth-Newton method is proven to converge globally and superlinearly for composite optimization with prox-regular nonconvex nonsmooth terms.
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