A key question in many low-rank problems throughout optimization, machine learning, and statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply these convex hulls ...

Submodular function optimisation has emerged as a cornerstone of contemporary algorithm design, offering a powerful framework to address a broad range of combinatorial problems characterised by the ...

The functions listed below are some of the common functions and datasets used for testing optimization algorithms. They are grouped according to similarities in their significant physical properties ...

Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer ...