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• W2952023151 abstract "We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize the sum of composite convex functions, where each term in the sum is a private cost function belonging to a node, and only nodes connected by an edge can directly communicate with each other. This optimization model abstracts a number of applications in distributed sensing and machine learning. We show that any limit point of DFAL iterates is optimal; and for any $epsilon>0$, an $epsilon$-optimal and $epsilon$-feasible solution can be computed within $mathcal{O}(log(epsilon^{-1}))$ DFAL iterations, which require $mathcal{O}(frac{psi_{max}^{1.5}}{d_{min}} epsilon^{-1})$ proximal gradient computations and communications per node in total, where $psi_{max}$ denotes the largest eigenvalue of the graph Laplacian, and $d_{min}$ is the minimum degree of the graph. We also propose an asynchronous version of DFAL by incorporating randomized block coordinate descent methods; and demonstrate the efficiency of DFAL on large scale sparse-group LASSO problems." @default.
• W2952023151 created "2019-06-27" @default.
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• W2952023151 date "2014-09-30" @default.
• W2952023151 modified "2023-09-27" @default.
• W2952023151 title "An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization" @default.
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