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 search ...

In this paper, we construct unconstrained methods for the generalized nonlinear complementarity problem and variational inequalities. Properties of the correspondent unconstrained optimization problem ...

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 ...

Below, you will find information about software developed by the Center, as well as download links. Disclaimer: This software is provided without any expressed or implied warranty. In particular, ...

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 ...

Description: Survey of computational tools for solving constrained and unconstrained nonlinear optimization problems. Emphasis on algorithmic strategies and characteristic structures of nonlinear ...