Systems | Information | Learning | Optimization
 

Analysis and Design of First-Order Methods for Smooth Strongly Convex Optimization & Low-Complexity Channel Estimation via the Sparse Fast Fourier Transform

Optimization algorithms play a fundamental role in analyzing the vast amount of data available today. Due to the need for fast optimization algorithms, there has been recent interest in understanding the mechanisms which enable optimization algorithms to converge quickly. We gain insight into these algorithms by leveraging techniques from control …

Nonconvex Distributed Optimization

We consider the distributed optimization problem where a group of agents seeks to cooperatively compute the optimizer of the average of local functions. To solve this problem, we propose a novel algorithm which adjusts the ratio between the number of communications and computations to achieve fast convergence. In particular, the …