报告主题：Complexity bounds for primal-dual methods minimizing the model of objective function
报告人： Yu. Nesterov 教授 比利时鲁汶大学((CORE), UCL, Belgium)
报告时间：2017年9月2日（周六）09:30
报告地点：校本部G507
邀请人： 白延琴教授
报告摘要：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.
报告人简介： 比利时鲁汶大学Yurii Nesterov教授是俄罗斯籍数学家，他优化领域国际最顶尖的学者之一。他的工作引领了近十年凸优化算法的发展，包括其在压缩感知、机器学习等方向的应用；他关于凸优化的教科书《Introductory Lectures on Convex Optimization》也成为优化领域的经典著作。其本人是优化领域最高奖Dantzig奖和信息科学领域最高奖之一冯诺伊曼奖的获得者。
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