6 citations · 10 across the 5 of their papers we have counts for
7 papers
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…
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Majid Jahani, Sergey Rusakov, Zheng Shi +3
We present a novel adaptive optimization algorithm for large-scale machine learning problems. Equipped with a low-cost estimate of local curvature and Lipschitz smoothness, our met…
DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction
Soheil Sadeghi Eshkevari, Martin Takáč, Shamim N. Pakzad +1
Data-driven models for predicting dynamic responses of linear and nonlinear systems are of great importance due to their wide application from probabilistic analysis to inverse pro…
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Majid Jahani, Mohammadreza Nazari, Rachael Tappenden +2
This work presents a new algorithm for empirical risk minimization. The algorithm bridges the gap between first- and second-order methods by computing a search direction that uses…
Distributed Fixed Point Methods with Compressed Iterates
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev +3
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated…