Mohammadreza Chamanbaz — Research — Randomized Methods

Randomized Methods in Analysis and Control of Uncertain Systems

Randomized algorithms are algorithms that make random choices during execution to produce results. They are used in analysis as well as control of uncertain systems. In this line of research, the uncertain parameters are considered as random variables. This enables us to extract random samples from the uncertainty set and asses the probabilistic performance of a candidate solution. We have developed novel sequential and non-sequential randomized algorithms to solve uncertain convex as well as non-convex optimization problems.

This work is done in collaboration with Prof. Roberto Tempo and Dr. Fabrizio Dabbene.

Publications

  • M. Chamanbaz, F. Dabbene, R. Tempo, V. Venkataramanan, and Q.-G. Wang, “Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty,” IEEE Transactions on Automatic Control, 2016, DOI, PDF.

  • M. Chamanbaz, F. Dabbene, R. Tempo, V. Venkataramanan, and Q.-G. Wang, “A Statistical Learning Theory Approach for Uncertain Linear and Bilinear Matrix Inequalities,” Automatica, Volume 50, Issue 6, June 2014, Pages 1617-1625, DOI, PDF.

  • M. Chamanbaz, F. Dabbene, D. Peaucelle and R. Tempo, “R-Romuloc: A unified tool for randomized and robust multiobjective control”, in Proc. 8th IFAC symposium on Robust Control Design, 2015, Bratislava, Slovak Republic, Pages 144-149, DOI, PDF.

  • M. Chamanbaz, F. Dabbene, R. Tempo, V. Venkataramanan, and Q.-G. Wang, “Sequential Randomized Algorithms for Sampled Convex Optimization,” in Proc. 2013 IEEE Multi-Conference on Systems and Control, Hyderabad, India, 2013, Pages 182-187, DOI, PDF. (Best Student Paper Award Finalist)

  • M. Chamanbaz, F. Dabbene, R. Tempo, V. Venkataramanan, and Q.-G. Wang, “On the Sample Complexity of Uncertain Linear and Bilinear Matrix Inequalities,” in Proc. IEEE 52nd Annual Conference on Decision and Control (CDC), 2013, Pages 1780-1785, DOI ,PDF.