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The Linear Quadratic Regulator (LQR) framework considers the problem of regulating a linear dynamical system perturbed by environmental noise.
Online control with adversarial disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham M Kakade, and Karan Singh · 1902
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Contributions to the theory of optimal control
Rudolf Emil Kalman et al · 1960
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Maximum likelihood estimates of linear dynamic systems
Herbert E Rauch, F Tung, and Charlotte T Striebel · 1965
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On the method of bounded differences
Colin McDiarmid · 1989
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Optimal control and estimation
Robert F Stengel · 1994
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Linear estimation
Thomas Kailath, Ali H Sayed, and Babak Hassibi · 2000
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Online computation and competitive analysis
Allan Borodin and Ran El-Yaniv · 2005
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Regret bounds for the adaptive control of linear quadratic systems
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Tracking adversarial targets
Yasin Abbasi-Yadkori, Peter Bartlett, and Varun Kanade · 2014
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Introduction to online convex optimization
Elad Hazan et al · 2016
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Thinking fast and slow: Optimization decomposition across timescales
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Logarithmic regret for online control
Naman Agarwal, Elad Hazan, and Karan Singh
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Online linear quadratic control
Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, and Kunal Talwar · 2018
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Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit
Yingying Li, Guannan Qu, and Na Li · 2018
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An online algorithm for smoothed regression and lqr control
Gautam Goel and Adam Wierman · 2019
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Beyond online balanced descent: An optimal algorithm for smoothed online optimization
Gautam Goel, Yiheng Lin, Haoyuan Sun, and Adam Wierman · 2019
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Online optimal control with linear dynamics and predictions: Algorithms and regret analysis
Yingying Li, Xin Chen, and Na Li · 2019
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