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math.OC2026
A Globally Convergent Newton Method for Nonsmooth Optimization in the absence of Smooth Surrogates
Tran T. A. Nghia, Nghia V. Vo, Khoa V. H. Vu
We propose a globally convergent Newton method for convex composite optimization problems with polyhedral regularizers. The method rests on the observation that the proximal gradie…
math.OC2026
Isolated calmness of regularized linear inverse problems
Tran T. A. Nghia, Huy N. Pham, Nghia V. Vo +1
This paper studies the isolated calmness property of solution mappings arising from convex regularized linear inverse problems. We mainly establish a tangent-cone characterization…
math.OC2026
Inexact DC Algorithms in Hilbert Spaces with Applications to PDE-Constrained Optimization
P. D. Khanh, V. V. H. Khoa, B. S. Mordukhovich +2
In this paper, we design and apply novel inexact adaptive algorithms to deal with minimizing difference-of-convex (DC) functions in Hilbert spaces. We first introduce I-ADCA, an in…