SOAR (Stochastic Optimization in Algorithmic Research) Lab is a research group in the Department of Systems and Industrial Engineering at the University of Arizona led by Dr. Afrooz Jalilzadeh.
Research Goals
Research at SOAR is centered on the design, analysis, and implementation of stochastic approximation methods. These methods play a pivotal role in solving complex convex optimization and stochastic variational inequality problems. Our work extends to diverse applications in machine learning, game theory, and power systems. By focusing on these critical areas, we strive to develop robust and efficient solutions that have a profound impact on advancing technology and decision-making processes across a wide range of industries.
Top News
- Interested to join our lab? Check here.
- Awarded an NSF grant: “Generalized Stochastic Nash Equilibrium Framework: Theory, Computation, and Application“.
- Awarded a Faculty Seed Grant, University of Arizona: “Mitigating Adversarial Attacks Using Generalized Nash Equilibrium”.
- Our paper on “A Stochastic Variance-reduced Accelerated Primal-Dual Method for Finite-sum Saddle-point Problems” is published in Computational Optimization and Applications.