A user manual for cuHALLaR: A GPU accelerated low-rank semidefinite programming Solver

We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs). Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA and a new enhanced data format taking advantage of Hybrid Sparse Low-Rank (HSLR) structure. The interface allows users to load … Read more

cuHALLaR: A GPU accelerated low-rank augmented Lagrangian method for large-scale semidefinite programming

This paper introduces cuHALLaR, a GPU-accelerated implementation of the HALLaR method proposed in Monteiro et al. 2024 for solving large-scale semidefinite programming (SDP) problems. We demonstrate how our Julia-based implementation efficiently uses GPU parallelism through optimization of simple, but key, operations, including linear maps, adjoints, and gradient evaluations. Extensive numerical experiments across three problem classes—maximum … Read more

Operation of an ambulance fleet under uncertainty

We introduce two new optimization models for the dispatch of ambulances. These models are to our knowledge the first providing a full modelling of the operation of an ambulance fleet, taking into account all or almost all constraints of the problem. The first model, called the ambulance selection problem, is used when an emergency call … Read more