This course introduces high-performance computing (“HPC”) systems, software, and methods used to solve large-scale problems in science and engineering. It will focus on the intersection of two ...

A thorough understanding of Linear Algebra and Vector Calculus, and strong familiarity with the Python programming language (e.g., basic data manipulation libraries, how to construct functions and ...

The eigenvalue complementarity problem (EiCP) represents a class of mathematical challenges where the determination of eigenvalues and corresponding eigenvectors is constrained by complementarity ...

The course is designed to provide engineering students a view of optimization as a tool for engineering decision making. Students will be given a fundamental introduction to the optimization ...

We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

With the advent of massively parallel computing coprocessors, numerical optimization for deep-learning disciplines is now possible. Complex real-time pattern recognition, for example, that can be used ...

This paper presents a numerical comparison between algorithms for unconstrained optimization that take account of sparsity in the second derivative matrix of the objective function. Some of the ...

Optical systems employ a rich array of physical effects which are described by well-understood equations. However, for all but the simplest devices these equations are typically too complex to permit ...

"With our technique, you know what you're getting in terms of performance because the numerical model and experimental ... One of the most advanced computational design techniques is known as topology ...