260 citations · 1.8k across the 63 of their papers we have counts for
11 papers · 1 filter
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points
Rachael Tappenden, Martin Takáč
This work shows that applying Gradient Descent (GD) with a fixed step size to minimize a (possibly nonconvex) quadratic function is equivalent to running the Power Method (PM) on t…
Optimal Power Flow Pursuit in the Alternating Current Model
Jie Liu, Antonio Bellon, Andrea Simonetto +2
Transmission-constrained problems in power systems can be cast as polynomial optimization problems whose coefficients vary over time. We consider the complications therein and sugg…
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences
Artem Agafonov, Brahim Erraji, Martin Takáč
In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order Framework via Compression and Sketching), the second-order framework FLECS was proposed for the…
Learning to Control under Time-Varying Environment
Yuzhen Han, Ruben Solozabal, Jing Dong +3
This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, des…
On Scaled Methods for Saddle Point Problems
Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev +2
Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning pr…
Learning to generalize Dispatching rules on the Job Shop Scheduling
Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal +1
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not…
Algorithm for Constrained Markov Decision Process with Linear Convergence
Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina +3
The problem of constrained Markov decision process is considered. An agent aims to maximize the expected accumulated discounted reward subject to multiple constraints on its costs…
Stochastic Gradient Methods with Preconditioned Updates
Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori +3
This work considers the non-convex finite sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly…
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
Guangyi Liu, Arash Amini, Martin Takac +1
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of…
Distributed Learning With Sparsified Gradient Differences
Yicheng Chen, Rick S. Blum, Martin Takac +1
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communica…
The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii +6
Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper,…