Papers under review
Y. Laguel, N. S. Aybat, M. Gürbüzbalaban, “High probability and riskaverse guarantees for stochastic saddle point problems,” under review.
A. Jalilzadeh, E. Yazdandoost Hamedani, and N. S. Aybat, “A DoublyRandomized BlockCoordinate PrimalDual Method for Largescale Saddle Point Problems," under review.
Accepted/Published Papers
X. Zhang, N. S. Aybat, M. Gürbüzbalaban, “Robust Accelerated PrimalDual Methods for Computing Saddle Points,” is accepted to SIAM Journal on Optimization (SIOPT).
D. Ghaderyan, N. S. Aybat, A. P. Aguiar, F. L. Pereira, “A Fast RowStochastic Decentralized Optimization Method Over Directed Graphs,” is accepted to IEEE Transactions on Automatic Control.
B. Can, S. Soori, N. S. Aybat, M. Dehvani, and M. Gurbuzbalaban, “Randomized Gossiping with Effective Resistance Weights: Performance Guarantees and Applications," IEEE Transactions on Control of Network Systems, 9(2), pp. 524536, June 2022.
N. S. Aybat, H. Ahmadi, and Uday V. Shanbhag, “On the Analysis of Inexact Augmented Lagrangian Schemes for Misspecified Conic Convex Programs,” IEEE Transactions on Automatic Control, 67(8), pp. 39813996, Aug. 2022.
E. Yazdandoost Hamedani and N. S. Aybat, “A decentralized primaldual method for constrained minimization of a strongly convex function, IEEE Transactions on Automatic Control, 67(11), pp. 56825697, Nov. 2022.
E. Yazdandoost Hamedani and N. S. Aybat, “A PrimalDual Algorithm with Line Search for General ConvexConcave Saddle Point Problems,” SIAM Journal on Optimization (SIOPT), 31(2), pp. 12991329, 2021.
S. Davanloo Tajbakhsh, N. S. Aybat, and Enrique del Castillo, “On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach” Journal of Machine Learning Research (JMLR), 21, pp. 141, 2020.
N. S. Aybat, A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, “Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions,” SIAM Journal on Optimization, 30(1), pp. 717751 (2019).
N. S. Aybat, E. Yazdandoost Hamedani, “A Distributed ADMMlike Method for Resource Sharing over Timevarying Directed Networks," SIAM Journal on Optimization, 29(4), pp. 3036–3068 (2019).
J. Wang, M. Ashour, C. M. Lagoa, N. S. Aybat, H. Che, “A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks," Automatica, 109, 108506 (2019).
S. Ma and N. S. Aybat, “Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants,” accepted to be published in the Proceedings of the IEEE, 2018.
M. Ashour, J. Wang, N. S. Aybat, C. Lagoa, and H. Che, “EndtoEnd Distributed Flow Control for Networks with Nonconcave Utilities,” accepted to be published in Transactions on Network Science and Engineering, 2017.
Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, and Enrique del Castillo, “Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets,” Statistica Sinica, 28 (2018), pp. 941962.
Necdet Serhat Aybat, Zi Wang, Tianyi Lin, and Shiqian Ma, “Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization,” IEEE Transactions on Automatic Control, 63 (2018), pp.520.
Ashkan Jasour, Necdet Serhat Aybat, and Constantino Lagoa, “Semidefinite Programming for Chance Constrained Optimization over Semialgebraic Sets,” SIAM Journal on Optimization, 25 (2015), pp. 14111440.
Necdet Serhat Aybat and Garud Iyengar, “An Alternating Direction Method with Increasing Penalty for Stable Principal Component Pursuit,” Computational Optimization and Applications, 61 (2015), pp. 635668.
Necdet Serhat Aybat and Garud Iyengar, “A Unified Approach for Minimizing Composite Norms,” Mathematical Programming, Series A, 144 (2014), pp. 181226.
Necdet Serhat Aybat, Shiqian Ma and Donald Goldfarb, “Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems,” Computational Optimization and Applications, 58 (2014), pp. 129.
Necdet Serhat Aybat and Garud Iyengar, “A FirstOrder Augmented Lagrangian Method for Compressed Sensing,” SIAM Journal on Optimization, 22 (2012), pp. 429459.
Necdet Serhat Aybat and Garud Iyengar, “A FirstOrder Smoothed Penalty Method for Compressed Sensing,” SIAM Journal on Optimization, 21 (2011), pp. 287313.
Technical Reports
Necdet Serhat Aybat and Zi Wang, “A Parallelizable Dual Smoothing Method for Large Scale Convex Regression Problems,” 2016.
Necdet Serhat Aybat and Garud Iyengar, “An Augmented Lagrangian Method for Conic Convex Programming,” 2013.
Necdet Serhat Aybat and Amit Chakraborty, “Fast Reconstruction of CT Images from Parsimonious Angular Measurements,” 2011.
Peer Reviewed Conference Papers
X. Zhang, G. MancinoBall, N. S. Aybat, and Y. Xu, “Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization,” is accepted to the 38th Annual AAAI Conference on Artificial Intelligence (acceptance rate 23.75%).
E. Yazdandoost Hamedani, A. Jalilzadeh, and N. S. Aybat, “Randomized PrimalDual Methods with LineSearch for Saddle Point Problems,” Proceeding of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, April 2527, 2023 (acceptance rate 29%).
X. Zhang, N. S. Aybat, M. Gürbüzbalaban, “SAPD+: An Accelerated Stochastic Method for NonconvexConcave Minimax Problems,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, November 28  December 9, 2022 (acceptance rate 25.6%, 2665 / 10411).
N. S. Aybat, A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, “A Universally Optimal Multistage Accelerated Stochastic Gradient Method,” Advances in Neural Information Processing Systems (NeurIPS), 32, pp. 85238534 (2019).
M. Ashour, C. M. Lagoa, and N. S. Aybat. “Lp Quasinorm Minimization,” 2019 53rd Asilomar Conference on Signals, Systems, and Computers, IEEE, pp. 726730 (2019).
N. S. Aybat, and M. Gurbuzbalaban, “Decentralized Computation of Effective Resistances and Acceleration of Consensus Algorithms,” the Proceedings of 2017 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, Canada, November 1416, 2017, pp. 538542.
E. Yazdandoost Hamedani, and N. S. Aybat, “Multiagent Constrained Optimization of a Strongly Convex Function,” the Proceedings of 2017 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, Canada, November 1416, 2017, pp. 558562.
E. Yazdandoost Hamedani, and N. S. Aybat, “Multiagent constrained optimization of a strongly convex function over timevarying directed networks/,” the Proceedings of 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, 2017, pp. 518525.
J. Wang, M. Ashour, C. Lagoa, N. S. Aybat, and H. Che, “Nonconcave network utility maximization in connectionless networks: A fully distributed traffic allocation algorithm,” the Proceedings of the 2017 American Control Conference (ACC), Seattle, WA, USA, May 2426, 2017, pp. 39803985.
M. Ashour, J. Wang, C. Lagoa, N. S. Aybat, and H. Che, “NonConcave Network Utility Maximization: A Distributed Optimization Approach,” the Proceedings of IEEE INFOCOM 2017  The 36th Annual IEEE International Conference on Computer Communications, Atlanta, GA, USA, May 14, 2017, pp. 19 (acceptance rate 20.93%, 292 / 1395).
N. S. Aybat, and E. Yazdandoost Hamedani, “A primaldual method for conic constrained distributed optimization problems,” Advances in Neural Information Processing Systems 29 (2016), pp.50495057. (Acceptance rate 22.72%, 568 / 2500).
E. Yazdandoost Hamedani, and N. S. Aybat, “Distributed primaldual method for multiagent sharing problem with conic constraints,” 2016 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2016, pp. 777782.
Hesam Ahmadi, Necdet Serhat Aybat, and Uday V. Shanbhag, “On the rate analysis of inexact augmented Lagrangian schemes for convex optimization problems with misspecified constraints,” the Proceedings of 2016 American Control Conference (ACC), Boston, MA, USA (2016), pp. 48414846.
Necdet Serhat Aybat, Zi Wang, and Garud Iyengar, “An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization,” Journal of Machine Learning Research (JMLR): W&CP 37 (2015), pp. 24542462 – Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015 (acceptance rate 26.03%, 270 / 1037).
Necdet Serhat Aybat, Sahar Zarmehri and Soundar Kumara, “An ADMM Algorithm for Clustering Partially Observed Networks,” the Proceedings of the 2015 SIAM International Conference on Data Mining (2015), pp. 460468. (Acceptence Rate: 14.66%) Download SDM15 Presentation
Necdet Serhat Aybat and Zi Wang, “A Parallel Method for Large Scale Convex Regression Problems,” the Proceedings of the IEEE Conference on Decision and Control (2014), pp. 57105717.
Necdet Serhat Aybat, Sinem Daysal, Burcu Tan and Fulden Topaloglu, “Decision Making Tests with Different Variations of the Stock Management Game,” the Proceedings of the 22nd International System Dynamics Conference (2004), Oxford, UK.
Chapters in Collections & Books
N. S. Aybat, “Algorithms for Stable PCA” in Handbook of Robust Low Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing, edited by T. Bouwmans, N. S. Aybat, and E. Zahzah, CRC Press, Taylor and Francis Group, 2016.
IMPORTANT: The first print of the book has typos in chapter “Algorithms for Stable PCA.” Download the corrected version
Handbook of Robust Low Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing, edited by T. Bouwmans, N. S. Aybat, and E. Zahzah, CRC Press, Taylor and Francis Group, 2016
Theses
Necdet Serhat Aybat, “First Order Methods for LargeScale Sparse Optimization,” PhD Thesis, 2011.
Necdet Serhat Aybat, “Analysis and Solution of Cardinality Constrained Quadratic Portfolio Optimization Problem Using Eigen Portfolios,” MS Thesis, 2005.
