activity
20242026
collaborators

8 papers

math.OC2026

Coderivative-Based Newton Methods in Structured Nonconvex and Nonsmooth Optimization

Pham Duy Khanh, Boris S. Mordukhovich, Vo Thanh Phat

This paper proposes and develops new Newton-type methods to solve structured nonconvex and nonsmooth optimization problems with justifying their fast local and global convergence b…

math.OC2026

Algebraic Farkas Lemma and Strong Duality for Perturbed Conic Linear Programming

P. D. Khanh, V. V. H. Khoa, T. H. Mo

This paper addresses the study of algebraic versions of Farkas lemma and strong duality results in the very broad setting of infinite-dimensional conic linear programming in dual p…

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…

math.OC2025

Convergence of First-Order Algorithms with Momentum from the Perspective of an Inexact Gradient Descent Method

Pham Duy Khanh, Boris Mordukhovich, Dat Ba Tran

This paper introduces a novel inexact gradient descent method with momentum (IGDm) considered as a general framework for various first-order methods with momentum. This includes, i…

math.OC2025

Characterizations of Variational Convexity and Tilt Stability via Quadratic Bundles

Pham Duy Khanh, Boris S. Mordukhovich, Vo Thanh Phat +1

In this paper, we establish characterizations of variational -convexity and tilt stability for prox-regular functions in the absence of subdifferential continuity via quadratic…

math.OC2025

Second-Order Subdifferential Optimality Conditions in Nonsmooth Optimization

Pham Duy Khanh, Vu Vinh Huy Khoa, Boris S. Mordukhovich +1

The paper is devoted to deriving novel second-order necessary and sufficient optimality conditions for local minimizers in rather general classes of nonsmooth unconstrained and con…