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- W4288284803 abstract "Thanks to its versatility, its simplicity, and its fast convergence, ADMM is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine tuning of system parameters according to the specific application scenario, and which ultimately calls for a thorough understanding of the hidden mechanisms that control the convergence behaviour. In this framework we aim at providing new theoretical insights on the convergence process and specifically on some constituent matrices of ADMM whose eigenstructure provides a close link with the algorithm's convergence speed. One of the key technique that we develop allows to effectively locate the eigenvalues of a (symmetric) matrix product, thus being able to estimate the contraction properties of ADMM. In the comparison with the results available from the literature, we are able to strengthen the precision of our speed estimate thanks to the fact that we are solving a joint problem (i.e., we are identifying the spectral radius of the product of two matrices) in place of two separate problems (the product of two matrix norms)." @default.
- W4288284803 created "2022-07-28" @default.
- W4288284803 creator A5066420511 @default.
- W4288284803 date "2019-07-08" @default.
- W4288284803 modified "2023-09-24" @default.
- W4288284803 title "New results on the local linear convergence of ADMM: a joint approach" @default.
- W4288284803 doi "https://doi.org/10.48550/arxiv.1907.03823" @default.
- W4288284803 hasPublicationYear "2019" @default.
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