Abstract
This article studies a distributed convex optimization problem with nonsmooth local objective functions subject to local inequality constraints and a coupled equality constraint. By combining the dual decomposition technique and subgradient flow method, a new distributed solution is developed in continuous time. Unlike the existing related continuous-time schemes either depending on specific initial conditions or on differentiability or strict (even strong) convexity of local cost functions, this study is free of initialization and takes into account general convex local objective functions which could be nonsmooth. Via nonsmooth analysis and set-valued LaSalle invariance principle, it is proved that a global optimal solution can be asymptotically obtained. Finally, the effectiveness of our algorithm is illustrated by numerical examples.
| Original language | English |
|---|---|
| Article number | 8957281 |
| Pages (from-to) | 4914-4921 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 65 |
| Issue number | 11 |
| Early online date | 13 Jan 2020 |
| DOIs | |
| Publication status | Published - Nov 2020 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
This work was supported by the National Natural Science Foundation of China under Grant 61673077, Grant 61773081, Grant 61860206008, Grant 61833013, and in part by the Chongqing Major Theme Program under Grant cstc2017zdcy-zdzxX0002.
Keywords
- Constrained optimization
- distributed convex optimization
- multiagent systems
- nonsmooth analysis