5 papers
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
Weiqin Chen, Mark S. Squillante, Chai Wah Wu +1
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as…
Stable Iterative Solvers for Ill-conditioned Linear Systems
Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu
Iterative solvers for large-scale linear systems such as Krylov subspace methods can diverge when the linear system is ill-conditioned, thus significantly reducing the applicabilit…
Stable iterative refinement algorithms for solving linear systems
Chai Wah Wu, Mark S. Squillante, Vasileios Kalantzis +1
Iterative refinement (IR) is a popular scheme for solving a linear system of equations based on gradually improving the accuracy of an initial approximation. Originally developed t…
On Mixed-Precision Iterative Methods and Analysis for Nearly Completely Decomposable Markov Processes
Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu
In this paper we consider the problem of computing the stationary distribution of nearly completely decomposable Markov processes, a well-established area in the classical theory o…
Breaking through the classical Shannon entropy limit: A new frontier through logical semantics
Luis A. Lastras, Barry M. Trager, Jonathan Lenchner +4
Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key asp…