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Globally Convergent Coderivative-Based Generalized Newton Methods in Nonsmooth Optimization by Khanh, Pham Duy; Mordukhovich, Boris; Phat, Vo Thanh; Tran, Dat Ba is a scholarly article available to read on EtoBox.

What is Globally Convergent Coderivative-Based Generalized Newton Methods in Nonsmooth Optimization about?

This paper proposes and justifies two globally convergent Newton-type methods to solve unconstrained and constrained problems of nonsmooth optimization by using tools of variational analysis and generalized differentiation. Both methods are coderivative-based and employ generalized Hessians (coderivatives of subgradient mappings) associated with objective functions, which are either of class $\mathcal{C}^{1,1}$, or are represented in the form of convex composite optimization, where one of the terms may be extended-real-valued. The proposed globally convergent algorithms are of two types. The first one extends the damped Newton method and requires positive-definiteness of the generalized Hessians for its well-posedness and efficient performance, while the other algorithm is of {the regularized Newton type} being well-defined when the generalized Hessians are merely positive-semidefinite. The obtained convergence rates for both methods are at least linear, but become superlinear under the semismooth$^*$ property of subgradient mappings. Problems of convex composite optimization are investigated with and without the strong convexity assumption {on smooth parts} of objective functions

Author

Khanh, Pham Duy; Mordukhovich, Boris; Phat, Vo Thanh; Tran, Dat Ba

Published

2021

Language

EN