Affiliation(s):
Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China;
moreAffiliation(s): Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China; College of Mathematics and Computerpar Science, Zhejiang Normal University, Jinhua 321004, China; School of Mathematical Sciences, Fudan University, Shanghai 200433, China; School of Automation, Central South University, Changsha 410083, China;
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Zicong XIA, Yang LIU, Wenlian LU, Weihua GUI. Matrix-valued distributed stochastic optimization with constraints[J]. Frontiers of Information Technology & Electronic Engineering , 1998, -1(5): .
@article{title="Matrix-valued distributed stochastic optimization with constraints", author="Zicong XIA, Yang LIU, Wenlian LU, Weihua GUI", journal="Frontiers of Information Technology & Electronic Engineering", volume="-1", number="-1", pages="", year="1998", publisher="Zhejiang University Press & Springer", doi="10.1631/FITEE.2200381" }
%0 Journal Article %T Matrix-valued distributed stochastic optimization with constraints %A Zicong XIA %A Yang LIU %A Wenlian LU %A Weihua GUI %J Frontiers of Information Technology & Electronic Engineering %V -1 %N -1 %P %@ 1869-1951 %D 1998 %I Zhejiang University Press & Springer
TY - JOUR T1 - Matrix-valued distributed stochastic optimization with constraints A1 - Zicong XIA A1 - Yang LIU A1 - Wenlian LU A1 - Weihua GUI J0 - Frontiers of Information Technology & Electronic Engineering VL - -1 IS - -1 SP - EP - %@ 1869-1951 Y1 - 1998 PB - Zhejiang University Press & Springer ER -
Abstract: In this paper, we address matrix-valued distributed stochastic optimization with inequality and equality constraints, where the objective function is a sum of multiple matrix-valued functions with stochastic variables and the considered problems are solved in a distributed manner. A penalty method is derived to deal with the constraints, and a selection principle is proposed for choosing feasible penalty functions and penalty gains. A distributed optimization algorithm based on the gossip model is developed for solving the stochastic optimization problem, and its convergence to the optimal solution is analyzed rigorously. Two numerical examples are delineated to demonstrate the viability of the main results.
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