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數學系Seminar第2064期 Linearized Proximal Algorithms for Convex Composite Optimization with Applications

創建時間:  2020/12/16  龔惠英   瀏覽次數:   返回

    數學系 Seminar 第 2063期

報告主題:Linearized Proximal Algorithms for Convex Composite Optimization with Applications

報 告 人:胡耀華 副教授 (深圳大學)

報告時間:2020年12月17日(周四) 9:30

會議地點:F309

邀 請 人:徐姿

主辦部門:理學院數學系

報告摘要: In this talk, we consider the convex composite optimization (CCO) problem that provides a unified framework of a wide variety of important optimization problems, such as convex inclusions, penalty methods for nonlinear programming, and regularized minimization problems. We will introduce a linearized proximal algorithm (LPA) to solve the CCO. The LPA has the attractive computational advantages of simple implementation and fast convergence rate. Under the assumptions of local weak sharp minima of Holderian order and a quasi-regularity condition, we establish a local/semi-local/global superlinear convergence rate for the LPA-type algorithms. We further apply the LPA to solve a (possibly nonconvex) feasibility problem, as well as a sensor network localization problem. Our numerical results illustrate that the LPA meets the demand for an efficient and robust algorithm for the sensor network localization problem.


歡迎教師、學生參加!

上一條:數學系Seminar第2065期 Smoothing fast iterative hard thresholding algorithm for L0 regularized nonsmooth convex regression problem

下一條:數學系Seminar第2063期 指向工程技術的數學——從研究方式到思維方式


數學系Seminar第2064期 Linearized Proximal Algorithms for Convex Composite Optimization with Applications

創建時間:  2020/12/16  龔惠英   瀏覽次數:   返回

    數學系 Seminar 第 2063期

報告主題:Linearized Proximal Algorithms for Convex Composite Optimization with Applications

報 告 人:胡耀華 副教授 (深圳大學)

報告時間:2020年12月17日(周四) 9:30

會議地點:F309

邀 請 人:徐姿

主辦部門:理學院數學系

報告摘要: In this talk, we consider the convex composite optimization (CCO) problem that provides a unified framework of a wide variety of important optimization problems, such as convex inclusions, penalty methods for nonlinear programming, and regularized minimization problems. We will introduce a linearized proximal algorithm (LPA) to solve the CCO. The LPA has the attractive computational advantages of simple implementation and fast convergence rate. Under the assumptions of local weak sharp minima of Holderian order and a quasi-regularity condition, we establish a local/semi-local/global superlinear convergence rate for the LPA-type algorithms. We further apply the LPA to solve a (possibly nonconvex) feasibility problem, as well as a sensor network localization problem. Our numerical results illustrate that the LPA meets the demand for an efficient and robust algorithm for the sensor network localization problem.


歡迎教師、學生參加!

上一條:數學系Seminar第2065期 Smoothing fast iterative hard thresholding algorithm for L0 regularized nonsmooth convex regression problem

下一條:數學系Seminar第2063期 指向工程技術的數學——從研究方式到思維方式

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