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Seminar第1505期 Complexity bounds for primal-dual methods minimizing the model of objective function

创建日期 2017-8-30 福平   浏览次数  124 返回    
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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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