My research interests are in the area of continuous optimization and algorithmic complexity. I am especially interested in the intersection of convex algebraic geometry and Semidefinite Programming.

• (Major) Theory and complexity analysis: Development of efficient algorithms for large-scale linear programming, convex quadratic programming, semidefinite programming, variational inequalities, and nonlinear convex programming.
• (Minor) Computational optimization: Developing fast numerical codes for GPU-accelerated first-order solvers for large-scale optimization.

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. Efficient algorithms for Bicriteria Traffic Assignment (Working paper)

Jacob M. Aguirre, Renato D.C. Monteiro, Anton J. Kleywegt. Complexity Analysis and Implementation of an accelerated smoothing gradient method (Working paper)

Jacob M. Aguirre and Renato D.C. Monteiro. A New Memory-efficient method for Sparse Plus Low Rank Semidefinite Programming

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

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