11 citations · 18 across the 14 of their papers we have counts for
45 papers · 1 filter
A Damped Newton Method Achieves Global and Local Quadratic Convergence Rate
Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk +3
In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an $O\left( \frac 1 {k…
Numerical Methods for Large-Scale Optimal Transport
Nazarii Tupitsa, Pavel Dvurechensky, Darina Dvinskikh +1
The optimal transport (OT) problem is a classical optimization problem having the form of linear programming. Machine learning applications put forward new computational challenges…
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…
An Approach for Non-Convex Uniformly Concave Structured Saddle Point Problem
Mohammad Alkousa, Alexander Gasnikov, Pavel Dvurechensky +2
Recently, saddle point problems have received much attention due to their powerful modeling capability for a lot of problems from diverse domains. Applications of these problems oc…
Vaidya's method for convex stochastic optimization in small dimension
Egor Gladin, Alexander Gasnikov, Elena Ermakova
This paper considers a general problem of convex stochastic optimization in a relatively low-dimensional space (e.g., 100 variables). It is known that for deterministic convex opti…
Stochastic optimization for dynamic pricing
Dmitry Pasechnyuk, Pavel Dvurechensky, Sergey Omelchenko +1
We consider the problem of supply and demand balancing that is stated as a minimization problem for the total expected revenue function describing the behavior of both consumers an…