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Revisiting Strong Duality, Hidden Convexity, and Gradient Dominance in the Linear Quadratic Regulator
The Linear Quadratic Regulator (LQR) is a cornerstone of optimal control theory, widely studied in both model-based and model-free approaches. Despite its well-established nature, certain foundational aspects remain subtle. In this paper, we revisit three key properties of policy optimization in LQR
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