The Hedge Algorithm on a Continuum


Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:824-832, 2015.


We consider an online optimization problem on a subset S of R^n (not necessarily convex), in which a decision maker chooses, at each iteration t, a probability distribution x^(t) over S, and seeks to minimize a cumulative expected loss, where each loss is a Lipschitz function revealed at the end of iteration t. Building on previous work, we propose a generalized Hedge algorithm and show a O(\sqrtt \log t) bound on the regret when the losses are uniformly Lipschitz and S is uniformly fat (a weaker condition than convexity). Finally, we propose a generalization to the dual averaging method on the set of Lebesgue-continuous distributions over S.

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