My research interests lie in the area of continuous optimization and complexity of algorithms.

• (Major) Theory and complexity analysis: Development of efficient algorithms for large-scale linear programming, convex quadratic programming, semidefinite programming, complementarity problems, variational inequalities, and nonlinear convex programming.
• (Minor) Computational optimization: Development of fast numerical methods and software for large-scale optimization, with an emphasis on GPU-accelerated first-order solvers.

Jacob M. Aguirre, Renato D.C. Monteiro, Anton J. Kleywegt. Hybrid Approaches for Large-Scale Linear Programs (Working paper)

Jacob M. Aguirre, Anton J. Kleywegt, Renato D.C. Monteiro. An Efficient Method for the Bicriterion Traffic Assignment Problem. In submission to Operations Research.

Jacob M. Aguirre, Renato D.C. Monteiro, Anton J. Kleywegt. Iteration Complexity of an Accelerated Smoothing Gradient Method. In submission to Mathematics of Operations Research.

Jacob M. Aguirre, Diego Cifuentes, Vincent Guigues, Renato D.C. Monteiro, Victor Hugo Nascimento, Arnesh Sujanani. cuHALLaR: A GPU Accelerated Low-Rank Augmented Lagrangian Method for Large-Scale Semidefinite Programming. Preprint here. In submission to Mathematical Programming Computation.

Jacob M. Aguirre, Diego Cifuentes, Vincent Guigues, Renato D.C. Monteiro, Victor Hugo Nascimento, Arnesh Sujanani. A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver. Preprint here.

Jacob Aguirre, Shrey Patel, Ruirui Ma. A Review of High Dimensional Nonlinear Dimension Reduction Methods Undergrad course paper