A Splitting Method for Optimal Control

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How researchers see this paper

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Variational Bayesian Reinforcement Learning with Regret Bounds (2018, methodology): “This problem is an exponential cone program (see appendix for details) which can be solved efficiently using modern methods [61, 62, 27, 92, 65].”
Optimal Parameter Selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic Problems (2013, methodology): “We have chosen to focus on quadratic problems, since they allow for analytical tractability, yet have vast applications in estimation [13], multi-agent systems [14] and control [15].”
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (2013, unknown): “5 can improve convergence, in practice [44,69].”
Operator Splitting for a Homogeneous Embedding of the Linear Complementarity Problem (2020, methodology): “This limitation, and the myriad of real-world applications with quadratic objectives, has inspired the development of ﬁrst-order ADMM based solvers that tackle the quadratic objective directly [59, 68, 26].”
Proximal Algorithms (2013, background): “[155] for a much more detailed discussion of using ADMM for problems of this type.”