Afrooz Jalilzadeh

Interests

Teaching

Linear and Nonlinear Programming,Stochastic Optimization,Probability and Statistics

Research

Stochastic optimization,Variational inequalities and Nash games,Risk averse optimization,Machine Learning,Healthcare optimization

Courses

Scholarly Contributions

Journals/Publications

• Alizadeh, Z., Jalilzadeh, A., & Yousefian, F. (2023). Randomized Lagrangian Stochastic Approximation for Large-Scale Constrained Stochastic Nash Games”. Optimization Letters.
• Jalilzadeh, A., Yousefian, F., & Ebrahimi, M. (2023). Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games. ACM Transactions on Modeling and Computer Simulation.
• Yazdandoost Hamedani, E., & Jalilzadeh, A. (2023). A Stochastic Variance-reduced Accelerated Primal-dual Method for Finite-sum Saddle-point Problems. Journal of Computational Optimization and Applications.
• Bardakci, I. E., Jalilzadeh, A., Lagoa, C., & Shanbhag, U. V. (2022). Probability Maximization via Minkowski Functionals: Convex Representations and Tractable Resolution. Mathematical Programming.
• Jalilzadeh, A., Shanbhag, U. V., Blanchet, J. H., & Glynn, P. W. (2022). Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs. Stochastic Systems.
• Jalilzadeh, A. (2021). Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. Optimization Letters.
• Jalilzadeh, A., Nedich, A., Shanbhag, U. V., & Yousefian, F. (2021). A variable sample-size stochastic quasi-Newton method for smooth and nonsmooth stochastic convex optimization. Mathematics of Operations Research.
• Jalilzadeh, A., Lei, J., & Shanbhag, U. V. (2019). Open Problem—Iterative Schemes for Stochastic Optimization: Convergence Statements and Limit Theorems. Stochastic Systems.

Proceedings Publications

• Yazdandoost Hamedani, E., Jalilzadeh, A., & Aybat, N. S. (2023). Randomized Primal-Dual Methods with Adaptive Step Sizes. In Artificial Intelligence and Statistics (AISTATS).
• Alizadeh, Z., Otero, B. M., & Jalilzadeh, A. (2022). An Inexact Variance-Reduced Method For Stochastic Quasi-Variational Inequality Problems With An Application In Healthcare. In 2022 Winter Simulation Conference (WSC).
• Boroun, M., & Jalilzadeh, A. (2021). Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems. In 2021 Winter Simulation Conference (WSC).
• Jalilzadeh, A., & Shanbhag, U. V. (2019). Smoothed First-order Algorithms for Expectation-valued Constrained Problems. In 2019 53rd Annual Conference on Information Sciences and Systems (CISS).
  More info

  We consider the development of first-order algorithms for convex stochastic optimization problems with expectation constraints. By recasting the problem as a solution to a monotone stochastic variational inequality problem, we note that a solution to this problem can be obtained as a solution to an unconstrained nonsmooth convex stochastic optimization problem. We utilize a variance-reduced smoothed first-order scheme for resolving such a problem and derive rate statements for such a scheme.
• Jalilzadeh, A., & Shanbhag, U. V. (2019, December). A proximal-point algorithm with variable sample-sizes (PPAWSS) for monotone stochastic variational inequality problems. In 2019 Winter Simulation Conference (WSC).
• Jalilzadeh, A., Nedich, A., Shanbhag, U. V., & Yousefian, F. (2018, December). A variable sample-size stochastic quasi-Newton method for smooth and nonsmooth stochastic convex optimization. In 2018 IEEE Conference on Decision and Control (CDC).
• Jalilzadeh, A., & Shanbhag, U. V. (2016, December). eg-VSSA: An extragradient variable sample-size stochastic approximation scheme: Error analysis and complexity trade-offs. In 2016 Winter Simulation Conference (WSC).

Presentations

• Jalilzadeh, A. (2022, Summer). Complexity Guarantees for Nonlinearly Constrained Nonsmooth Stochastic Convex-Concave Minimax Optimization. International Conference on Continuous Optimization (ICCOPT).
• Jalilzadeh, A. (2021, Fall). Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. INFORMS Annual Meeting.
• Jalilzadeh, A. (2020, Fall). Presenting "Iteration Complexity Of Randomized Primal-dual Methods For Convex-concave Saddle Point Problems". INFORMS annual meeting.
• Jalilzadeh, A. (2019, Fall). Rate Analysis For Variance-reduced Stochastic Quasi-newton Schemes For Stochastic Convex Optimization. INFORMS annual meeting.
• Jalilzadeh, A. (2018, Fall). Smoothing and Acceleration for Stochastic Convex Optimization. INFORMS annual meeting.
• Jalilzadeh, A. (2017, Fall). On Variable Sample-sizeStochastic Mirror-descent and Fista-like Schemes for Nonsmooth Stochastic Optimization. INFORMS annual meeting.

Poster Presentations

• Jalilzadeh, A. (2016, June). A Variable Sample-Size Stochastic Approximation Scheme (VSSA) : Rate analysis and Complexity Trade-offs. ICML: Optimization Methods for the Next Generation of Machine Learning.