Back to Search
Start Over
Decentralized Randomized Block-Coordinate FrankâWolfe Algorithms for Submodular Maximization Over Networks.
- Source :
-
IEEE Transactions on Systems, Man & Cybernetics. Systems . Aug2022, Vol. 52 Issue 8, p5081-5091. 11p. - Publication Year :
- 2022
-
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 $(1-e^{-p_{\max }/p_{\min }})$ of the global maximal points at a rate of $\mathcal {O}(1/T)$ by choosing a suitable stepsize, where $T$ is the number of iterations. In addition, we confirm the theoretical results by experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 52
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 158186115
- Full Text :
- https://doi.org/10.1109/TSMC.2021.3112691