Jacob Aguirre

I am a 2nd year Operations Research PhD Candidate at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Institute of Technology. I am grateful to be advised by Professors Anton J. Kleywegt and Renato D.C. Monteiro, and quite fortunate to collaborate and learn from Dr. Arkadi Nemirovski. My research interests lie in the area of continuous optimization and complexity of algorithms. More specifically, I am interested in the theory, complexity analysis, and implementation of algorithms for solving large-scale optimization programs.

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

  • Jacob Aguirre
  • Diego Cifuentes
  • Vincent Guigues
  • Renato D.C. Monteiro
  • Victor Hugo Nascimento
  • Arnesh Sujanani
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems, Semi-definite Programming Tags , , , , , ,

    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

  • Jacob Aguirre
  • Diego Cifuentes
  • Vincent Guigues
  • Renato D.C. Monteiro
  • Victor Hugo Nascimento
  • Arnesh Sujanani
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming, Optimization Software and Modeling Systems Tags , , , , ,

    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