10 papers
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…
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…
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.…
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…