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Workshop “Frontiers of High Dimensional Statistics, Optimization, and Econometrics”
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[Complexity bounds for primal-dual methods minimizing the model of objective function] Ю. Е. Нестеров Université Catholique de Louvain |
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Аннотация: We provide Frank-Wolfe (Conditional Gradients) method with a convergence analysis allowing to approach a primal-dual solution of convex optimization problem with composite objective function. Additional properties of complementary part of the objective (strong convexity) significantly accelerate the scheme. We also justify a new variant of this method, which can be seen as a trust-region scheme applying the linear model of objective function. Our analysis works also for a quadratic model, allowing to justify the global rate of convergence for a new second-order method. To the best of our knowledge, this is the first trust-region scheme supported by the worst-case complexity bound. Язык доклада: английский |
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