E6602 is typically taught once per year in the Spring semester. The information below is meant to provide a snapshot of the material covered. Students will learn to recognize, model, formulate and ...

Abstract: We put forth a hybrid-computing solution to a class of constrained nonlinear optimization problems involving nonlinear cost and linear constraints. This is accomplished by realizing gradient ...

Behind every powerful AI model lies a world of mathematics—linear algebra, calculus, probability, and more—that makes it all work. Understanding these concepts bridges the gap between theory and ...

This repository contains code used to perform acoustic parameter estimation using Bayesian optimization with a Gaussian process surrogate model. The following papers use this code: William Jenkins, ...

Abstract: In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved ...