15 papers · 1 filter
Decentralised convex optimisation with probability-proportional-to-size quantization
Dmitrii Pasechniuk, Pavel Dvurechensky, César A. Uribe +1
Communication is one of the bottlenecks of distributed optimisation and learning. To overcome this bottleneck, we propose a novel quantization method that transforms a vector into…
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…
Effects of momentum scaling for SGD
Dmitry A. Pasechnyuk, Alexander Gasnikov, Martin Takáč
The paper studies the properties of stochastic gradient methods with preconditioning. We focus on momentum updated preconditioners with momentum coefficient . Seeking to explain…
Stochastic optimization in digital pre-distortion of the signal
A. V. Alpatov, E. A. Peters, D. A. Pasechnyuk +1
In this paper, we test the performance of some modern stochastic optimization methods and practices in application to digital pre-distortion problem, that is a valuable part of pro…
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…
Non-convex optimization in digital pre-distortion of the signal
Dmitry Pasechnyuk, Alexander Maslovskiy, Alexander Gasnikov +9
In this paper, we give some observation of applying modern optimization methods for functionals describing digital predistortion (DPD) of signals with orthogonal frequency division…