15 papers · 1 filter
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…
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…
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…
On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients
Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6
First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…
Lipschitz-Free Mirror Descent Methods for Non-Smooth Optimization Problems
Bowen Yuan, Mohammad S. Alkousa
The part of the analysis of the convergence rate of the mirror descent method that is connected with the adaptive time-varying step size rules due to Alkousa et al. (MOTOR 2024, pp…
Mirror Descent Methods with Weighting Scheme for Outputs for Constrained Variational Inequality Problems
Mohammad S. Alkousa, Belal A. Alashqar, Fedor S. Stonyakin +2
This paper is devoted to the variational inequality problems. We consider two classes of problems, the first is classical constrained variational inequality and the second is the s…