Event-Triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation
The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mech...
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| Vydáno v: | IEEE transactions on signal processing Ročník 72; s. 2594 - 2606 |
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| Jazyk: | angličtina |
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2024
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| Abstract | The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA) is proposed. The analysis shows that if the event-triggered threshold converges to zero as time tends to infinity and the cumulative difference between consecutive optimal values is sublinear, the dynamic regret of EPOGDA is sublinear. Further, we extend the proposed EPOGDA to the gradient-free scenarios, where the gradients are estimated using the Gaussian smoothed gradient estimator (GSGE). The GSGE-EPOGDA is presented and analyzed, which does not lead to performance degradation as compared to EPOGDA. Finally, the advantages of EPOGDA and GSGE-EPOGDA are verified on a distributed multi-sensor network. |
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| AbstractList | The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA) is proposed. The analysis shows that if the event-triggered threshold converges to zero as time tends to infinity and the cumulative difference between consecutive optimal values is sublinear, the dynamic regret of EPOGDA is sublinear. Further, we extend the proposed EPOGDA to the gradient-free scenarios, where the gradients are estimated using the Gaussian smoothed gradient estimator (GSGE). The GSGE-EPOGDA is presented and analyzed, which does not lead to performance degradation as compared to EPOGDA. Finally, the advantages of EPOGDA and GSGE-EPOGDA are verified on a distributed multi-sensor network. |
| Author | Zhou, Yaoyao Chen, Gang |
| Author_xml | – sequence: 1 givenname: Yaoyao orcidid: 0009-0004-9050-8867 surname: Zhou fullname: Zhou, Yaoyao email: zhouyaoyao@cqu.edu.cn organization: College of Automation, Chongqing University, Chongqing, China – sequence: 2 givenname: Gang orcidid: 0000-0003-1098-6953 surname: Chen fullname: Chen, Gang email: chengang@cqu.edu.cn organization: College of Automation, Chongqing University, Chongqing, China |
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| SubjectTerms | Algorithms Convex functions Distributed parameter estimation dynamic regret Dynamic scheduling Estimation event-triggered mechanism Heuristic algorithms online convex optimization Parameter estimation Performance degradation proximal algorithm Sensors Signal processing algorithms |
| Title | Event-Triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation |
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