1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Numerical analysis of the convex relaxation of the barrier parameter functional of self-concordant barriers
Vitali Pirau, Roland Hildebrand
Self-concordant barriers are essential for interior-point algorithms in conic programming. To speed up the convergence it is of interest to find a barrier with the lowest possible…
Fast convergence of sample-average approximation for saddle-point problems
Vitali Pirau
Stochastic saddle point (SSP) problems are, in general, less studied compared to stochastic minimization problems. However, SSP problems emerge from machine learning (adversarial t…
On the relations of stochastic convex optimization problems with empirical risk minimization problems on -norm balls
Darina Dvinskikh, Vitali Pirau, Alexander Gasnikov
In this paper, we consider convex stochastic optimization problems arising in machine learning applications (e.g., risk minimization) and mathematical statistics (e.g., maximum lik…