Privacy Preservation for Distributed Nonsmooth Constrained Optimization Based on Pseudo-Subgradient

被引：0

作者：

Zeng, Xianlin ^{[1
]}

Liang, Shu ^{[2
]}

Chen, Jie ^{[3
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China

[2] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China

[3] Beijing Inst Technol, Minist Educ, Beijing Adv Innovat Ctr Intelligent Robots & Syst, Key Lab Biomimet Robots & Syst, Beijing 100081, Peoples R China

来源：

2018 37TH CHINESE CONTROL CONFERENCE (CCC) | 2018年

基金：

中国博士后科学基金;

关键词：

Distributed nonsmooth convex optimization; privacy preservation; set constraints; pseudo-subgradients; ALGORITHM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we investigate a privacy preservation design in the distributed nonsmooth convex optimization with set constraints. To solve the distributed optimization problem while preserving the privacy, we use pseudo-subgradients involved with (non-integrable) set-valued functions. Based on pseudo-subgradients, we propose distributed nonsmooth optimization algorithms with keeping subgradient information confidential. Then we prove the correctness and convergence of the distributed privacy preservation optimization algorithms to achieve the exact solution of the original optimization problem.

引用

页码：2400 / 2405

页数：6

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