activity
20182026
most citedGeneralized Mirror Prox for Monotone Variational Inequalities: Universality and Inexact Oracle

15 citations · 54 across the 38 of their papers we have counts for

collaborators

41 papers

cs.LG2026

Clipped or Unclipped? Finite-Sample Trade-offs for Averaged SGD under Heavy-Tailed Noise

Alexandra Suvorikova, Egor Gladin, Darina Dvinskikh +5

Gradient clipping is widely used to stabilize training, but it need not improve the statistical accuracy of averaged SGD, even under heavy-tailed noise. We derive a finite-sample c…

math.NA2026

Iterative Methods for Computing the Moore--Penrose Inverse of Split-Quaternion Matrices with Applications

Salman Ahmadi-Asl, Valentin Leplat, Mohammad S. Alkousa

We study iterative methods for computing the Moore--Penrose inverse of split-quaternion matrices. We first establish a consistent framework based on a \(2\times2\) real representat…

math.OC2026

Mirror Descent Methods for Quasar Convex Optimization Problems With Non-Smooth Inequality Constraints

Mohammad Alkousa

In this paper, we consider constraint optimization problems subject to non-smooth convex functional (inequality-type) constraints, wherein the objective function is non-smooth and…

cs.LG2026

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar +1

Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models.…

math.OC2026

Lipschitz-Free Mirror Descent Methods for Relatively Strongly Convex Functions with/without Absolute and Relative Inexactness

Mohammad S. Alkousa, Fedor S. Stonyakin

In this paper, we analyze the mirror descent algorithm for non-smooth optimization problems in which the objective function is relatively strongly convex, without relying on the st…

math.OC2025

Speeding up the Goemans-Williamson randomized procedure by difference-of-convex optimization

Hadi Salloum, Roland Hildebrand, Nhat Trung Nguyen +4

We present a novel approach to accelerate the Goemans-Williamson (GW) randomized rounding procedure for quadratic unconstrained binary optimization (QUBO) problems. Instead of solv…