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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.14612 |
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| _version_ | 1866913210129448960 |
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| author | Xia, Zhaoyue Du, Jun Jiang, Chunxiao Poor, H. Vincent Han, Zhu Ren, Yong |
| author_facet | Xia, Zhaoyue Du, Jun Jiang, Chunxiao Poor, H. Vincent Han, Zhu Ren, Yong |
| contents | With the growth of magnitude of multi-agent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs towards an absolute probability sequence $π$ is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multi-agent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions, and extended to non-convex objectives that satisfy Polyak-Lojasiewicz conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14612 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Inhomogeneous Infinite Products of Stochastic Matrices and Applications Xia, Zhaoyue Du, Jun Jiang, Chunxiao Poor, H. Vincent Han, Zhu Ren, Yong Optimization and Control Systems and Control With the growth of magnitude of multi-agent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs towards an absolute probability sequence $π$ is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multi-agent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions, and extended to non-convex objectives that satisfy Polyak-Lojasiewicz conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework. |
| title | On Inhomogeneous Infinite Products of Stochastic Matrices and Applications |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2401.14612 |