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• W2973657717 abstract "In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed controllers can be learned with sub-linear sample complexity and computed with near-linear time complexity, both measured with respect to the dimension of the system. In particular, we provide sharp end-to-end guarantees on the stability and the performance of the designed distributed controller and prove that for sparse systems, the number of samples needed to guarantee robust and near optimal performance of the designed controller can be significantly smaller than the dimension of the system. Finally, we show that the proposed optimization problem can be solved to global optimality with near-linear time complexity by iteratively solving a series of small quadratic programs." @default.
• W2973657717 created "2019-09-26" @default.
• W2973657717 creator A5052941508 @default.
• W2973657717 creator A5061966752 @default.
• W2973657717 creator A5072786751 @default.
• W2973657717 date "2019-09-21" @default.
• W2973657717 modified "2023-09-27" @default.
• W2973657717 title "Efficient Learning of Distributed Linear-Quadratic Controllers." @default.
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