Optimization problems are common in computer engineering, where you need to find the best solution from a set of possible choices under some constraints. For example, you might want to minimize the ...

Some problems are so hard that finding an exact solution would take too long, even with the most powerful computers. These problems are called intractable, and they often arise in fields like ...

Abstract: Multiprocessor task scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant ...

Combinatorial optimisation problems arise in many fields, from logistics and network design to machine learning and bioinformatics. Most classical formulations are NP-hard, rendering exact ...

Abstract: Noncommutative constraint satisfaction problems (CSPs) are higher-dimensional operator extensions of classical CSPs. Their approximability remains largely unexplored. A notable example of a ...

This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick \(k\) centers with the minimum clustering cost such that ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...

This course studies approximation algorithms – algorithms that are used for solving hard optimization problems. Such algorithms find approximate (slightly suboptimal) solutions to optimization ...

This project explores the NP-hard problem of scheduling unrelated parallel machines, a significant challenge in optimizing resource utilization. Specifically, it focuses on efficiently assigning ...