(Major) Convex optimization theory and algorithms with specific interests in:
- Investigating complexity and developing efficient algorithms for Stochastic and Nonlinear Programming
- Distributionally Robust Optimization
- Statistical Inference via Convex Optimization
Çaglar Çaglayan, Yunan Liu, Turgay Ayer, et al. Physician staffing in emergency rooms (ERs): Opening the black-box of ER care via a multi-class multi-stage network. (Under revision at Management Science.)
Renato D.C. Monteiro, Jacob M. Aguirre, Anton J. Kleywegt. A Perspective on Hybrid Frank-Wolfe Decompositions for scalable Semidefinite Programming. (Work in progress)
Jacob M. Aguirre, Renato D.C. Monteiro, Anton J. Kleywegt. Convex Relaxations for Rank One Symmetric Tensor Completion using Burer-Monteiro methods. (Work in progress)
Renato D.C. Monteiro and Jacob M. Aguirre. A Low-Rank Hybrid Augmented Lagrangian Method for Maximum Stable Set Problem. (Work in progress)
Jacob M. Aguirre. On Complexity of Tropical Ideals and Generic Semidefinite Programs. Preprint here.
Jacob Aguirre, Shrey Patel, Ruirui Ma.
A Review of High Dimensional Nonlinear Dimension Reduction Methods