A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization |
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| X. L. Yi, S. J. Zhang, T. Yang, T. Y. Chai, and K. H. Johansson, “A primal-dual SGD algorithm for distributed nonconvex optimization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 812–833, May 2022. doi: 10.1109/JAS.2022.105554 |
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| Authors: | Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson |
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| Affiliation: | 1. Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, and also affiliated with the DigitalFutures, Stockholm 10044, Sweden;2. Department of Electrical Engineering, University of North Texas, Denton, TX 76203 USA;3. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China |
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| Abstract: | The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchang... |
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| Keywords: | Distributed nonconvex optimization linear speedup Polyak-Łojasiewicz (P-Ł) condition primal-dual algorithm stochastic gradient descent |
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