Abstract
In this paper, we study the unconstrained optimization problem in a distributed way over directed strongly connected communication graphs. We propose an algorithm, which combines techniques of both gradient descent (GD) and finite-time exact ratio consensus (FTERC). Different from the techniques of average or dynamic average consensus with asymptotic convergence or techniques of finite-time “approximate” consensus with inexact accuracy in the literature, with the help of FTERC for gradient tracking, our proposed distributed FTERC based GD algorithm has a faster convergence rate related to the optimization iteration number and a larger step-size upper bound compared with other algorithms, as demonstrated in the simulations.
| Original language | English |
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| Title of host publication | 2022 IEEE 61st Conference on Decision and Control (CDC) |
| Publisher | IEEE |
| Pages | 6848-6854 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-6654-6761-2 |
| DOIs | |
| Publication status | Published - 2022 |
| MoE publication type | A4 Conference publication |
| Event | IEEE Conference on Decision and Control - Cancun, Mexico, Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 Conference number: 61 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision & Control |
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| ISSN (Electronic) | 2576-2370 |
Conference
| Conference | IEEE Conference on Decision and Control |
|---|---|
| Abbreviated title | CDC |
| Country/Territory | Mexico |
| City | Cancun |
| Period | 06/12/2022 → 09/12/2022 |
Keywords
- Gradient methods
- Upper bound
- Costs
- Additives
- Heuristic algorithms
- Directed graphs
- Approximation algorithms
- Distributed optimization
- gradient tracking
- finite-time consensus
- directed graphs
- gradient descent