Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems. They achieve efficiency by constructing ...

The BFGS method for unconstrained optimization, using a variety of line searches, including backtracking, is shown to be globally and superlinearly convergent on uniformly convex problems. The ...

In this work, we present a robust optimization method that is suited for unconstrained problems with a nonconvex cost function as well as for problems based on simulations, such as large partial ...

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

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...