(Corrected Version) Push-LSVRG-UP: Distributed Stochastic Optimization over Unbalanced Directed Networks with Uncoordinated Triggered Probabilities
-д хадгалсан:
| -д хэвлэсэн: | arXiv.org (Dec 18, 2024), p. n/a |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , |
| Хэвлэсэн: |
Cornell University Library, arXiv.org
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full text outside of ProQuest |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
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|---|---|---|---|
| 001 | 2814623829 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1109/TNSE.2022.3225229 |2 doi | |
| 035 | |a 2814623829 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Hu, Jinhui | |
| 245 | 1 | |a (Corrected Version) Push-LSVRG-UP: Distributed Stochastic Optimization over Unbalanced Directed Networks with Uncoordinated Triggered Probabilities | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Distributed stochastic optimization, arising in the crossing and integration of traditional stochastic optimization, distributed computing and storage, and network science, has advantages of high efficiency and a low per-iteration computational complexity in resolving large-scale optimization problems. This paper concentrates on resolving a large-scale convex finite-sum optimization problem in a multi-agent system over unbalanced directed networks. To tackle this problem in an efficient way, a distributed consensus optimization algorithm, adopting the push-sum technique and a distributed loopless stochastic variance-reduced gradient (LSVRG) method with uncoordinated triggered probabilities, is developed and named Push-LSVRG-UP. Each agent under this algorithmic framework performs only local computation and communicates only with its neighbors without leaking their private information. The convergence analysis of Push-LSVRG-UP is relied on analyzing the contraction relationships between four error terms associated with the multi-agent system. Theoretical results provide an explicit feasible range of the constant step-size, a linear convergence rate, and an iteration complexity of Push-LSVRG-UP when achieving the globally optimal solution. It is shown that Push-LSVRG-UP achieves the superior characteristics of accelerated linear convergence, fewer storage costs, and a lower per-iteration computational complexity than most existing works. Meanwhile, the introduction of an uncoordinated probabilistic triggered mechanism allows Push-LSVRG-UP to facilitate the independence and flexibility of agents in computing local batch gradients. In simulations, the practicability and improved performance of Push-LSVRG-UP are manifested via resolving two distributed learning problems based on real-world datasets. | |
| 653 | |a Algorithms | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Convergence | ||
| 653 | |a Complexity | ||
| 653 | |a Iterative methods | ||
| 653 | |a Optimization | ||
| 653 | |a Distributed processing | ||
| 653 | |a Computer networks | ||
| 700 | 1 | |a Chen, Guo | |
| 700 | 1 | |a Li, Huaqing | |
| 700 | 1 | |a Shen, Zixiang | |
| 700 | 1 | |a Zhang, Weidong | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2814623829/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2305.09181 |