A super-atomic norm minimization approach to identifying sparse dynamical graphical models
This paper considers the problem of identifying sparse dynamical graphical models from
input/output data. Our main result shows that this problem can be recast into an expanded
atomic-norm minimization framework that allows for enforcing block-sparsity. This approach
leads to efficient algorithms capable of handling large data sets, unknown inputs and
fragmented data records. These results are illustrated with several examples.
input/output data. Our main result shows that this problem can be recast into an expanded
atomic-norm minimization framework that allows for enforcing block-sparsity. This approach
leads to efficient algorithms capable of handling large data sets, unknown inputs and
fragmented data records. These results are illustrated with several examples.
This paper considers the problem of identifying sparse dynamical graphical models from input/output data. Our main result shows that this problem can be recast into an expanded atomic-norm minimization framework that allows for enforcing block-sparsity. This approach leads to efficient algorithms capable of handling large data sets, unknown inputs and fragmented data records. These results are illustrated with several examples.
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