Iterative algorithm for solving a class of convex feasibility problem

被引：2
|
作者：
Li, Chunmei [1 ]
Duan, Xuefeng [2 ]
Lu, Linzhang [1 ]
Wang, Qingwen [3 ]
Shen, Shuqian [4 ]
机构：
[1] Guizhou Normal Univ, Sch Math Sci, Guiyang 550001, Guizhou, Peoples R China
[2] Guilin Univ Elect Technol, Coll Math & Computat Sci, Guangxi Key Lab Cryptog & Informat Secur, Key Lab Data Anal & Computat,Guangxi Coll & Univ, Guilin 541004, Peoples R China
[3] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
[4] China Univ Petr, Coll Sci, Qingdao 257061, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2019年 / 352卷
基金：
中国国家自然科学基金;
关键词：
Convex feasibility problem; Projection formula; Relaxed alternating projection algorithm; Quantum computation; SUBGRADIENT PROJECTION ALGORITHMS;
D O I：
10.1016/j.cam.2018.11.029
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we consider a class of convex feasibility problem, which arises in quantum computation. Based on the matrix equation theory, the feasible sets are characterized by exploiting the special structure of the linear constraints, and its analytic expression is given. By making use of the nice structure properties and the KKT condition, we derive the projection formulas of a matrix onto the feasible sets. The relaxed alternating projection method is designed to solve the convex feasibility problem. Numerical experiments show that the new method is feasible and effective. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：352 / 367
页数：16
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