Randomized gradient-free distributed online optimization via a dynamic regret analysis
This work considers an online distributed optimization problem, with a group of agents whose local objective functions vary with time. Moreover, the value of the objective function is revealed to the corresponding agent after the decision is executed per time-step. Thus, each agent can only update t...
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| Main Authors: | , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/170697 |
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| 機構: | Nanyang Technological University |
| 語言: | English |