(Student authors are marked with * for grad and ** for undergraduate students)
Journal papers
Accepted/Published
- Alizadeh, Z*., Jalilzadeh, A., and Yousefian, F., ”Randomized Lagrangian Stochastic Approximation for Large-Scale Constrained Stochastic Nash Games”. Accepted for Publication in Optimization Letters, 2023. arXiv:2304.07688
- Jalilzadeh, A., and Yousefian, F., Ebrahimi M., ”Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games”. Accepted for Publication in ACM Transactions on Modeling and Computer Simulation, 2023. arXiv:2203.01271
- Yazdandoost Hamedani, E., Jalilzadeh, A. “A Stochastic Variance-reduced Accelerated Primal-dual Method for Finite-sum Saddle-point Problems”. Published in Computational Optimization and Applications, 2023.
- Bardakci I. e., Jalilzadeh A., Lagoa C., and Shanbhag U. V., “Probability Maximization via MinkowskiFunctionals: Convex Representations and Tractable Resolution”. Published in Mathematical Programming, 2022.
- Jalilzadeh A., Shanbhag U. V., Blanchet H., and Glynn P., “Optimal Smoothed Variable Sample-size Accelerated Proximal Methods for Structured Nonsmooth Stochastic Convex Programs”. Published in Stochastic Systems, 2022.
- Jalilzadeh, A. “Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems”. Published in Optimization Letters, 2021.
- Jalilzadeh, A., Nedich A., Shanbhag, U. V., and Yousefian, F., “A Variable Sample-size Stochastic Quasi-NewtonMethod for Smooth and Nonsmooth Stochastic Convex Optimization”. Published in Mathematics of Operations Research, 2021.
- Jalilzadeh A., Lei J., and Shanbhag U. V., “Open Problem-Iterative Schemes for Stochastic Optimization: Convergence Statements and Limit Theorems”. Published in Stochastic Systems 9.3 (2019): 299-302.
Under Review
- Gubbi, K. I., Latibari, B. S., Chowdhury, M. A., Jalilzadeh, A., Yazdandoost Hamedani, E., Rafatirad, S., Sasan, A., Homayoun, H., and Salehi, S., “Optimized and Automated Secure IC Design Flow: A Defense-in-Depth Approach”. Under review at IEEE Transactions on Circuits and Systems, 2023.
- Flores, O., Anani, A., Li, H., and Jalilzadeh, A. “Heuristic and Exact Approaches to Optimize the Production Scheduling of Mines Transitioning from Open Pit to Block Caving”. Under review at Mining, Metallurgy \& Exploration, 2023.
- Yazdandoost Hamedani, E., Jalilzadeh A., Aybat N. S., and Shanbhag U. V., “Iteration Complexity of Randomized Primal-Dual Methods for Convex-Concave Saddle Point Problems”. arXiv: 1806.04118.
Machine Learning Conference Papers
- Boroun, M.*, Yazdandoost Hamedani, E., and Jalilzadeh, A. “Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems“. Accepted in Conference on Neural Information Processing Systems (NeurIPS) 2023, arXiv:2306.11944.
- Yazdandoost Hamedani, E., Jalilzadeh, A., and Aybat, N. S., “Randomized Primal-Dual Methods with Adaptive Step Sizes.” International Conference on Artificial Intelligence and Statistics. PMLR, 2023.
Peer-Reviewed Conference Papers
- Alizadeh, Z.*, Polanco, F. P.**, and Jalilzadeh, A., “A Projection-Based Algorithm for Solving Stochastic Inverse Variational Inequality Problems”. Accepted at the 2023 Winter Simulation Conference (WSC).
- Boroun, M.,*, Alizadeh, Z.*, and A. Jalilzadeh, “Accelerated Primal-dual Scheme for a Class of Stochastic Nonconvex-concave Saddle Point Problems,” 2023 American Control Conference (ACC), San Diego, CA, USA, 2023, pp. 204-209.
- N. Abolfazli, A. Jalilzadeh, and E. Y. Hamedani, “An Accelerated Asynchronous Distributed Method for Convex Constrained Optimization Problems,” 2023 57th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2023, pp. 1-6
- Alizadeh, Z.*, Otero, B. M.**, and Jalilzadeh, A. “An Inexact Variance-Reduced Method For Stochastic Quasi-Variational Inequality Problems With An Application In Healthcare”. In Proceedings of the 2022 Winter Simulation Conference (WSC), Singapore.
- Boroun, M.*, Jalilzadeh, A. “Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems”. In Proceedings of the 2021 Winter Simulation Conference, Phoenix, AZ.
- Jalilzadeh, A., and Shanbhag, U. V. (2019) “A Proximal-point Algorithm with Variable Sample-sizes (PPAWSS)for Monotone Stochastic Variational Inequality Problems”, In Proceedings of the 2019 Winter SimulationConference, National Harbor, MD.
- Jalilzadeh, A., Nedich, A., Shanbhag, U. V., and Yousefian, F. (2018) “A Variable Sample-size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization”, In 2018 IEEE Conference on Decision and Control (CDC) pp. 4097-4102. IEEE.
- Jalilzadeh, A., and Shanbhag, U. V. (2016) “eg-VSSA: an extragradient variable sample-size stochastic approximation scheme: error analysis and complexity trade-offs”, In 2016 Winter Simulation Conference (WSC), pp. 690-701. IEEE.
Book Chapters
- Jalilzadeh, A. (2023). Stochastic Quasi-NewtonScheme. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-54621-2_830-1.