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%20of%20a%20sparse%2C%20high-dimensional%2C%20stationary%20matrix-variate%20Gaussian%20time%20series.%20All%20past%20work%20on%20high-dimensional%20matrix%20graphical%20models%20assumes%20that%20independent%20and%20identically%20distributed%20(i.i.d.)%20observations%20of%20the%20matrix-variate%20are%20available.%20Here%20we%20allow%20dependent%20observations.%20We%20consider%20a%20sparse-group%20lasso-based%20frequency-domain%20formulation%20of%20the%20problem%20with%20a%20Kronecker-decomposable%20power%20spectral%20density%20(PSD)%2C%20and%20solve%20it%20via%20an%20alternating%20direction%20method%20of%20multipliers%20(ADMM)%20approach.%20The%20problem%20is%20bi-convex%20which%20is%20solved%20via%20flip-flop%20optimization.%20We%20provide%20sufficient%20conditions%20for%20local%20convergence%20in%20the%20Frobenius%20norm%20of%20the%20inverse%20PSD%20estimators%20to%20the%20true%20value.%20This%20result%20also%20yields%20a%20rate%20of%20convergence.%20We%20illustrate%20our%20approach%20using%20numerical%20examples%20utilizing%20both%20synthetic%20and%20real%20data.)
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