Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910655708135424 |
|---|---|
| author | Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. |
| author_facet | Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. |
| contents | In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Systems and Control Optimization and Control In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. |
| title | Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2409.05418 |