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Hesameddin Mohammadi
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2020 – today
- 2023
- [j6]Ibrahim Kurban Özaslan, Hesameddin Mohammadi, Mihailo R. Jovanovic:
Computing Stabilizing Feedback Gains via a Model-Free Policy Gradient Method. IEEE Control. Syst. Lett. 7: 407-412 (2023) - [j5]Hesameddin Mohammadi, Samantha Samuelson, Mihailo R. Jovanovic:
Transient Growth of Accelerated Optimization Algorithms. IEEE Trans. Autom. Control. 68(3): 1823-1830 (2023) - [c15]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Noise amplifiation of momentum-based optimization algorithms. ACC 2023: 849-854 - [c14]Samantha Samuelson, Hesameddin Mohammadi, Mihailo R. Jovanovic:
Performance of noisy higher-order accelerated gradient flow dynamics for strongly convex quadratic optimization problems. ACC 2023: 3939-3844 - [c13]Samantha Samuelson, Hesameddin Mohammadi, Mihailo R. Jovanovic:
Performance of Noisy Three-Step Accelerated First-Order Optimization Algorithms for Strongly Convex Quadratic Problems. CDC 2023: 1300-1305 - 2022
- [j4]Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
Convergence and Sample Complexity of Gradient Methods for the Model-Free Linear-Quadratic Regulator Problem. IEEE Trans. Autom. Control. 67(5): 2435-2450 (2022) - [c12]Hesameddin Mohammadi, Mihailo R. Jovanovic:
On the noise amplification of primal-dual gradient flow dynamics based on proximal augmented Lagrangian. ACC 2022: 926-931 - [i4]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms. CoRR abs/2209.11920 (2022) - 2021
- [j3]Hesameddin Mohammadi, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
On the Linear Convergence of Random Search for Discrete-Time LQR. IEEE Control. Syst. Lett. 5(3): 989-994 (2021) - [j2]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems. IEEE Trans. Autom. Control. 66(6): 2480-2495 (2021) - [c11]Hesameddin Mohammadi, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information. CDC 2021: 1120-1124 - [i3]Hesameddin Mohammadi, Samantha Samuelson, Mihailo R. Jovanovic:
Transient growth of accelerated first-order methods for strongly convex optimization problems. CoRR abs/2103.08017 (2021) - 2020
- [j1]Armin Zare, Hesameddin Mohammadi, Neil K. Dhingra, Tryphon T. Georgiou, Mihailo R. Jovanovic:
Proximal Algorithms for Large-Scale Statistical Modeling and Sensor/Actuator Selection. IEEE Trans. Autom. Control. 65(8): 3441-3456 (2020) - [c10]Samantha Samuelson, Hesameddin Mohammadi, Mihailo R. Jovanovic:
Transient growth of accelerated first-order methods. ACC 2020: 2858-2863 - [c9]Hesameddin Mohammadi, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
Random search for learning the linear quadratic regulator. ACC 2020: 4798-4803 - [c8]Samantha Samuelson, Hesameddin Mohammadi, Mihailo R. Jovanovic:
On the transient growth of Nesterov's accelerated method for strongly convex optimization problems. CDC 2020: 5911-5916 - [c7]Hesameddin Mohammadi, Mihailo R. Jovanovic, Mahdi Soltanolkotabi:
Learning the model-free linear quadratic regulator via random search. L4DC 2020: 531-539
2010 – 2019
- 2019
- [c6]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Performance of noisy Nesterov's accelerated method for strongly convex optimization problems. ACC 2019: 3426-3431 - [c5]Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
Global exponential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator. CDC 2019: 7474-7479 - [i2]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Robustness of accelerated first-order algorithms for strongly convex optimization problems. CoRR abs/1905.11011 (2019) - [i1]Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi, Mihailo R. Jovanovic:
Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem. CoRR abs/1912.11899 (2019) - 2018
- [c4]Brendon K. Colbert, Hesameddin Mohammadi, Matthew M. Peet:
Combining SOS with Branch and Bound to Isolate Global Solutions of Polynomial Optimization Problems. ACC 2018: 2190-2197 - [c3]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
On the stability of gradient flow dynamics for a rank-one matrix approximation problem. ACC 2018: 4533-4538 - [c2]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Variance Amplification of Accelerated First-Order Algorithms for Strongly Convex Quadratic Optimization Problems. CDC 2018: 5753-5758 - 2017
- [c1]Morgan Jones, Hesameddin Mohammadi, Matthew M. Peet:
Estimating the region of attraction using polynomial optimization: A converse Lyapunov result. CDC 2017: 1796-1802
Coauthor Index
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