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Publications about 'Convergence rate'
Theses
  1. H. Mohammadi. Robustness of gradient methods for data-driven decision making. PhD thesis, University of Southern California, 2022. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Data-driven control, Gradient descent, Gradient-flow dynamics, Heavy-ball method, Integral quadratic constraints, Linear quadratic regulator, Model-free control, Nesterov's accelerated method, Nonconvex optimization, Nonnormal dynamics, Noise amplification, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity, Second-order moments, Transient growth. [bibtex-entry]


Journal articles
  1. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms. IEEE Trans. Automat. Control, 2022. Note: Submitted; also arXiv:2209.11920. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Nesterov's accelerated method, Nonnormal dynamics, Noise amplification, Second-order moments. [bibtex-entry]


Conference articles
  1. S. Samuelson and M. R. Jovanovic. Tradeoffs between convergence speed and noise amplification in first-order optimization: the role of averaging. In Proceedings of the 2024 American Control Conference, Toronto, Canada, 2024. Note: To appear. Keyword(s): Accelerated first-order algorithms, Averaging, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]


  2. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Noise amplification of momentum-based optimization algorithms. In Proceedings of the 2023 American Control Conference, San Diego, CA, pages 849-854, 2023. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Nesterov's accelerated method, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]


  3. I. K. Ozaslan and M. R. Jovanovic. Tight lower bounds on the convergence rate of primal-dual dynamics for equality constrained convex problems. In Proceedings of the 62nd IEEE Conference on Decision and Control, Singapore, pages 7312-7317, 2023. Keyword(s): Gradient flow dynamics, Exponential stability, Integral quadratic constraints, Primal-dual gradient flow dynamics, Primal-dual methods. [bibtex-entry]


  4. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. Performance of noisy higher-order accelerated gradient flow dynamics for strongly convex quadratic optimization problems. In Proceedings of the 2023 American Control Conference, San Diego, CA, pages 3839-3844, 2023. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]


  5. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. Performance of noisy three-step accelerated first-order optimization algorithms for strongly convex quadratic problems. In Proceedings of the 62nd IEEE Conference on Decision and Control, Singapore, pages 1300-1305, 2023. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Three-step momentum algorithm. [bibtex-entry]


  6. S. Hassan-Moghaddam and M. R. Jovanovic. On the exponential convergence rate of proximal gradient flow algorithms. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 4246-4251, 2018. Note: (Invited paper). Keyword(s): Control for optimization, Distributed optimization, Forward-backward envelope, Exponential convergence, Global exponential stability, Gradient flow dynamics, Large-scale systems, Non-smooth optimization, Primal-dual method, Proximal algorithms, Proximal augmented Lagrangian. [bibtex-entry]



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Last modified: Tue Jan 23 11:32:51 2024
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