Paper Info
Title
Decentralized Randomized Block-Coordinate Frank–Wolfe Algorithms for Submodular Maximization Over Networks
Abstract
We consider decentralized large-scale continuous submodular constrained optimization problems over networks, where the goal is to maximize a sum of nonconvex functions with diminishing returns property. However, the computations of the projection step and the whole gradient can become prohibitive in high-dimensional constrained optimization problems. For this reason, a decentralized randomized block-coordinate Frank-Wolfe algorithm is proposed for submoduar maximization over networks by local communication and computation, which adopts the randomized block-coordinate descent and the Frank-Wolfe technique. We also show that the proposed algorithm converges to an approximation fact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-e^{-p_{\max }/p_{\min }})$ </tex-math></inline-formula> of the global maximal points at a rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/T)$ </tex-math></inline-formula> by choosing a suitable stepsize, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the number of iterations. In addition, we confirm the theoretical results by experiments.
Year
DOI
Venue
2022
10.1109/TSMC.2021.3112691
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Conditional gradient methods,decentralized optimization,randomized block-coordinate descent,submodular maximization
Journal
52
Issue
ISSN
Citations 
8
2168-2216
0
PageRank 
References 
Authors
0.34
32
5
Name
Order
Citations
PageRank
Mingchuan Zhang12910.19
Yangfan Zhou223229.72
Quanbo Ge300.34
Ruijuan Zheng4255.25
Qingtao Wu5165.35