Designing smoothing functions for improved worst-case competitive ratio in online optimization

NeurIPS 2016 Reza EghbaliMaryam Fazel

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant... (read more)

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