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Mahdi Soltanolkotabi | Mihailo R. Jovanovi'c | Armin Zare | Hesameddin Mohammadi | M. Soltanolkotabi | Hesameddin Mohammadi | A. Zare | M. Jovanovi'c
[1] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[2] D. Kleinman. On an iterative technique for Riccati equation computations , 1968 .
[3] Sergey Levine,et al. Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[4] Mihailo R. Jovanovic,et al. Controller architectures: Tradeoffs between performance and structure , 2016, Eur. J. Control.
[5] Mikhail V. Khlebnikov,et al. An LMI approach to structured sparse feedback design in linear control systems , 2013, 2013 European Control Conference (ECC).
[6] Sham M. Kakade,et al. Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator , 2018, ICML.
[7] M. Fardad,et al. Sparsity-promoting optimal control for a class of distributed systems , 2011, Proceedings of the 2011 American Control Conference.
[8] Benjamin Recht,et al. A Tour of Reinforcement Learning: The View from Continuous Control , 2018, Annu. Rev. Control. Robotics Auton. Syst..
[9] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[10] Stephen P. Boyd,et al. Numerical Methods for H 2 Related Problems , 1992 .
[11] Zhi-Quan Luo,et al. An ADMM algorithm for optimal sensor and actuator selection , 2014, 53rd IEEE Conference on Decision and Control.
[12] H. Toivonen. A globally convergent algorithm for the optimal constant output feedback problem , 1985 .
[13] D. Bertsekas. Approximate policy iteration: a survey and some new methods , 2011 .
[14] W. Marsden. I and J , 2012 .
[15] Venkataramanan Balakrishnan,et al. Semidefinite programming duality and linear time-invariant systems , 2003, IEEE Trans. Autom. Control..
[16] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[17] Roman Vershynin,et al. High-Dimensional Probability , 2018 .
[18] Hannu T. Toivonen,et al. Newton's method for solving parametric linear quadratic control problems , 1987 .
[19] Mark W. Schmidt,et al. Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition , 2016, ECML/PKDD.
[20] Tamer Basar,et al. Policy Optimization for H2 Linear Control with H∞ Robustness Guarantee: Implicit Regularization and Global Convergence , 2020, L4DC.
[21] G. Dullerud,et al. A Course in Robust Control Theory: A Convex Approach , 2005 .
[22] Nikolai Matni,et al. On the Sample Complexity of the Linear Quadratic Regulator , 2017, Foundations of Computational Mathematics.
[23] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[24] Ekkehard W. Sachs,et al. Computational Design of Optimal Output Feedback Controllers , 1997, SIAM J. Optim..
[25] Tryphon T. Georgiou,et al. Proximal Algorithms for Large-Scale Statistical Modeling and Sensor/Actuator Selection , 2018, IEEE Transactions on Automatic Control.
[26] Mehran Mesbahi,et al. LQR through the Lens of First Order Methods: Discrete-time Case , 2019, ArXiv.
[27] M. Athans,et al. On the determination of the optimal constant output feedback gains for linear multivariable systems , 1970 .
[28] Boris Polyak,et al. Optimizing Static Linear Feedback: Gradient Method , 2020, SIAM J. Control. Optim..
[29] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[30] J. Ackermann. Parameter space design of robust control systems , 1980 .
[31] Adel Javanmard,et al. Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks , 2017, IEEE Transactions on Information Theory.
[32] Jose C. Geromel,et al. An alternate numerical solution to the linear quadratic problem , 1994, IEEE Trans. Autom. Control..
[33] M. Rudelson,et al. Hanson-Wright inequality and sub-gaussian concentration , 2013 .
[34] Nevena Lazic,et al. Model-Free Linear Quadratic Control via Reduction to Expert Prediction , 2018, AISTATS.
[35] Michael I. Jordan,et al. Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification , 2018, COLT.
[36] M. Vidyasagar,et al. Maximal Lyapunov Functions and Domains of Attraction for Autonomous Nonlinear Systems , 1981 .
[37] Avi Wigderson,et al. Sum-of-Squares Lower Bounds for Sparse PCA , 2015, NIPS.
[38] S. Bittanti,et al. The Riccati equation , 1991 .
[39] Bin Hu,et al. Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems , 2020, 2020 American Control Conference (ACC).
[40] Sham M. Kakade,et al. Global Convergence of Policy Gradient Methods for Linearized Control Problems , 2018, ICML 2018.
[41] Mahdi Soltanolkotabi,et al. Random search for learning the linear quadratic regulator , 2020, 2020 American Control Conference (ACC).
[42] Benjamin Recht,et al. Simple random search provides a competitive approach to reinforcement learning , 2018, ArXiv.
[43] Maryam Kamgarpour,et al. Learning the Globally Optimal Distributed LQ Regulator , 2019, L4DC.
[44] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[45] P. Olver. Nonlinear Systems , 2013 .
[46] Fu Lin,et al. Augmented Lagrangian Approach to Design of Structured Optimal State Feedback Gains , 2011, IEEE Transactions on Automatic Control.
[47] Kaiqing Zhang,et al. Policy Optimization for H2 Linear Control with H∞ Robustness Guarantee: Implicit Regularization and Global Convergence , 2019, SIAM J. Control. Optim..
[48] Fu Lin,et al. Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers , 2011, IEEE Transactions on Automatic Control.
[49] B. Anderson,et al. Optimal control: linear quadratic methods , 1990 .
[50] Martin J. Wainwright,et al. Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems , 2018, AISTATS.
[51] Mahdi Soltanolkotabi,et al. On the Linear Convergence of Random Search for Discrete-Time LQR , 2021, IEEE Control Systems Letters.