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 ...

In this paper we test different conjugate gradient (CG) methods for solving largescale unconstrained optimization problems. The methods are divided in two groups: the first group includes five basic ...

Purdue faculty dedicate countless hours to exploring the frontiers of their respective fields, pushing the boundaries of knowledge and contributing to the ever-evolving landscape of academia. To ...

Studies linear and nonlinear programming, the simplex method, duality, sensitivity, transportation and network flow problems, some constrained and unconstrained optimization theory, and the ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

Problem definition: Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the ...