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    學術預告:李沖教授談“Linearized proximal algorithms with adaptive stepsizes for convex composite optimization with applications”

    來源 : 理學院     作者 : 理學院     時間 : 2020-11-02  訪問量 : 62

    時間:11月3日(周二)15:00

    地點:勤園21號樓304

    摘要:

    In this talk, we will propose an inexact linearized proximal algorithm with an adaptive stepsize, together with its globalized version based on the backtracking line-search, to solve the convex composite optimization problem. Under the assumptions of local weak sharp minima of order p (p ≥ 1) for the outer convex function and a quasi-regularity condition for the inclusion problem associated to the inner function, we establish the superlinear/quadratic convergence results for the proposed algorithms. Compared to the linearized proximal algorithms with a constant stepsize proposed in [1], our algorithms own broader applications and higher convergence rates. Numerical applications to the nonnegative inverse eigenvalue problem and the wireless sensor network localization problem indicate that the proposed algorithms are more e?cient and robust, and outperform the algorithms proposed in [1] and some popular algorithms for relevant problems.

    References

    [1] Y. H. Hu, C. Li, and X. Q. Yang, On convergence rates of linearized proximal algorithms for convex composite optimization with applications, SIAM J. Optim., 26 (2016), pp. 1207–1235.

    專家簡介:

      李沖,浙江大學數學系教授,博士生導師?,F任《高等學校計算數學學報》以及多個國際刊物的編委。主要從事Banach空間理論、非光滑分析、非線性逼近與優化、數值泛函分析等領域的研究。先后主持中國國家自然科學基金、西班牙及南非國家自然科學基金等近二十項,出版專著1部,在SCI期刊上發表論文近200篇, 特別是在優化理論和計算數學的頂級刊物SIAM J Optim., Math. Program,SIAM J. Control Optim.以及SIAM J.Numer. Anal上發表論文近30篇。 曾獲浙江省教委科技進步獎一、二等獎等獎勵,享受國務院政府特殊津貼專家、原商業部有突出貢獻的中青年專家、江蘇省第七屆青年科學家等,2004年獲教育部首屆新世紀優秀人才計劃資助。


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