research
∙
06/22/2023
Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems
Moving horizon estimation (MHE) is a widely studied state estimation app...
share
research
∙
06/04/2022
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
Adaptive gradient methods have become popular in optimizing deep neural ...
research
∙
08/19/2021
On Accelerating Distributed Convex Optimizations
This paper studies a distributed multi-agent convex optimization problem...
research
∙
05/31/2021
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective
Accelerated gradient-based methods are being extensively used for solvin...
research
∙
01/26/2021
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
This paper considers the problem of multi-agent distributed linear regre...
research
∙
11/15/2020
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
This paper considers the multi-agent distributed linear least-squares pr...
research
∙
08/06/2020
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
This paper considers the multi-agent linear least-squares problem in a s...
research
∙
03/13/2020
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
Gradient-descent method is one of the most widely used and perhaps the m...
research
∙
12/06/2017