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 ...

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 ...

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 ...

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 ...

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 ...