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- W2889071720 abstract "We consider the problem of structured sparse approximation of a linear undirected consensus network. We formulate an optimization scheme that minimizes the performance deviation from that of a dense linear undirected consensus network, while promoting the sparsity of the sparse approximation assuming a pre-given arbitrary sparsity structure. It is shown that the resulting non-convex optimization problem can equivalently be reformulated as a rank-constrained problem. Next, we utilize the bi-linear rank penalty technique to find a sub-optimal structured sparse approximation which satisfies the rank constraint with arbitrary tolerance. Finally, a sub-optimal structured sparse approximation of a randomly generated network is employed to visualize the effectiveness of our proposed method." @default.
- W2889071720 created "2018-09-07" @default.
- W2889071720 creator A5043710051 @default.
- W2889071720 date "2018-06-01" @default.
- W2889071720 modified "2023-10-11" @default.
- W2889071720 title "Structured Sparse Approximation of a Linear Undirected Consensus Network" @default.
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- W2889071720 doi "https://doi.org/10.1109/icca.2018.8444264" @default.
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