Abstract
This article studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multiagent network, the objective function of which is a sum of agents' local convex cost functions. To solve such problem, a penalty-based distributed continuous-time subgradient algorithm with time-varying gain is developed for each agent to seek the saddle point of the penalty Lagrangian function. It is shown that an exact primal optimal solution can be obtained with certain assumption on time-varying gain. Moreover, the proposed algorithm adopts fixed-time projection scheme to ensure that for any initial state value, each local state estimate converges to its convex constraint set within fixed time. Finally, numerical examples are provided to show the effectiveness of the theoretical results.
| Original language | English |
|---|---|
| Pages (from-to) | 390-397 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 67 |
| Issue number | 1 |
| Early online date | 2 Feb 2021 |
| DOIs | |
| Publication status | Published - Jan 2022 |
| Externally published | Yes |
Bibliographical note
Recommended by Associate Editor G. Notarstefano.Publisher Copyright:
© 1963-2012 IEEE.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grants 62073048, 61860206008, 61773081, 61833013, 61933012, 61991403, 61991400, in part by the Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0264, in part by the Fundamental Research Funds for the Central Universities under Grant 2020CDJ-LHZZ001, and in part by the Zhejiang Lab under Grant 2019NB0AB06.
Keywords
- Continuous-time subgradient algorithm
- distributed optimization
- fixed-time projection
- time-varying digraph