[26] A. Nedic, A. Olshevsky, and M. G. Rabbat. Network topology and communication-computation tradeoffs in decentralized optimization. Proceedings of the IEEE, 106:953-976, 2018.
[27] Angela Nedic, Alex Olshevsky, and Michael G Rabbat. Network topology and communication-computation tradeoffs in decentralized optimization. Proceedings of the IEEE, 106(5):953-976, 2018.
[28] Angela Nedic, Alex Olshevsky, and Wei Shi. Achieving geometric convergence for distributed optimization over time-varying graphs. SIAM Journal on Optimization, 27(4):2597-2633, 2017.
[29] Y. Peng, J. Suo, Q. Dai, and W. Xu. Reweighted low-rank matrix recovery and its application in image restoration. IEEE Transactions on Cybernetics, 44(12):2418-2430, 2014.
[30] N. Razin, A. Maman, and N. Cohen. Implicit regularization in tensor factorization. In International Conference on Machine Learning, pages 8913-8924, 2021.
[31] N. Razin, A. Maman, and N. Cohen. Implicit regularization in hierarchical tensor factorization and deep convolutional neural networks. arXiv preprint, arXiv:2201.11729, 2022, 2022.
[32] G. Scutari and Y. Sun. Distributed nonconvex constrained optimization over time-varying digraphs. Mathematical Programming, 176:497-544, 2019.
[33] X. Shen and Y. Wu. A unified approach to salient object detection via low rank matrix recovery. In IEEE Conference on Computer Vision and Pattern Recognition, pages 853-860, 2012.
[34] Dominik Stöger and Mahdi Soltanolkotabi. Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. Advances in Neural Information Processing Systems, 34:23831-23843, 2021.
[35] Dominik Stöger and Mahdi Soltanolkotabi. Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction, 2022.
[36] Brian Swenson, Ryan Murray, H. Vincent Poor, and Soummya Kar. Distributed gradient flow: Nonsmoothness, nonconvexity, and saddle point evasion. IEEE Transactions on Automatic Control, 67(8):3949-3964, 2022.
[37] Tatiana Tatarenko and Behrouz Touri. Non-convex distributed optimization. IEEE Trans. on Autom. Control, 62(8):3744-3757, 2017.
[38] Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Ben Recht. Low-rank solutions of linear matrix equations via procrustes flow. In International Conference on Machine Learning, pages 964-973. PMLR, 2016.
[39] Stefan Vlaski and Ali H. Sayed. Distributed learning in non-convex environments—part ii: Polynomial escape from saddle-points. IEEE Transactions on Signal Processing, 69:1257–1270, 2021.
[40] Stefan Vlaski and Ali H. Sayed. Distributed learning in non-convex environments—part i: Agreement at a linear rate. IEEE Transactions on Signal Processing, 69:1242–1256, 2021.
[41] Stefan Vlaski and Ali H. Sayed. Second-order guarantees of stochastic gradient descent in nonconvex optimization. IEEE Transactions on Automatic Control, 67(12):6489-6504, 2022.
[42] H.T. Wai, J. Lafond, A. Scaglione, and E. Moulines. Decentralized frank-wolfe algorithm for convex and non-convex problems. IEEE Transactions on Automatic Control, 62(11):5522-5537, 2017.
[43] Lei Wang and Xin Liu. Smoothing gradient tracking for decentralized optimization over the stiefel manifold with non-smooth regularizers. arXiv preprint arXiv:2303.15882, 2023.
[44] Xiaolu Wang, Yuchen Jiao, Hoi-To Wai, and Yuantao Gu. Incremental aggregated riemannian gradient method for distributed pca. In International Conference on Artificial Intelligence and Statistics, pages 7492-7510. PMLR, 2023.