典型文献
Limited Memory BFGS Method for Least Squares Semidefinite Programming with Banded Structure
文献摘要：
This work is intended to solve the least squares semidefinite program with a banded struc-ture.A limited memory BFGS method is presented to solve this structured program of high dimension.In the algorithm,the inverse power iteration and orthogonal iteration are employed to calculate partial eigenvectors instead of full decomposition of n×n matrices.One key feature of the algorithm is that it is proved to be globally convergent under inexact gradient information.Preliminary numerical results indicate that the proposed algorithm is comparable with the inexact smoothing Newton method on some large instances of the structured problem.
文献关键词：
中图分类号：
作者姓名：
XUE Wenjuan;SHEN Chungen;YU Zhensheng
作者机构：
School of Mathematics and Physics,Shanghai University of Electric Power,Shanghai 200090,China;University of Shanghai for Science and Technology,Shanghai 200093,China
文献出处：
引用格式：
[1]XUE Wenjuan;SHEN Chungen;YU Zhensheng-.Limited Memory BFGS Method for Least Squares Semidefinite Programming with Banded Structure)[J].系统科学与复杂性学报（英文版）,2022(04):1500-1519
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Limited,Memory,BFGS,Method,Least,Squares,Semidefinite,Programming,Banded,Structure,This,work,intended,solve,least,squares,semidefinite,program,banded,limited,memory,method,presented,this,structured,high,dimension,In,algorithm,inverse,power,iteration,orthogonal,employed,calculate,partial,eigenvectors,instead,full,decomposition,matrices,One,key,feature,that,proved,be,globally,convergent,under,inexact,gradient,information,Preliminary,numerical,results,indicate,proposed,comparable,smoothing,Newton,some,large,instances,problem
AB值：
0.706861
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