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+of+a+sparse%252C+high-dimensional%252C+stationary+matrix-variate+Gaussian+time+series.+All+past+work+on+high-dimensional+matrix+graphical+models+assumes+that+independent+and+identically+distributed+(i.i.d.)+observations+of+the+matrix-variate+are+available.+Here+we+allow+dependent+observations.+We+consider+a+sparse-group+lasso-based+frequency-domain+formulation+of+the+problem+with+a+Kronecker-decomposable+power+spectral+density+(PSD)%252C+and+solve+it+via+an+alternating+direction+method+of+multipliers+(ADMM)+approach.+The+problem+is+bi-convex+which+is+solved+via+flip-flop+optimization.+We+provide+sufficient+conditions+for+local+convergence+in+the+Frobenius+norm+of+the+inverse+PSD+estimators+to+the+true+value.+This+result+also+yields+a+rate+of+convergence.+We+illustrate+our+approach+using+numerical+examples+utilizing+both+synthetic+and+real+data.&cdnurl=https%3a%2f%2fbing.ai.mrmusic.cloudns.ch/th%2fth%2fid%2fR.7e1edf429e9701386b63357d8add5ed9%3frik%3dL9UOx8VV0cWPEQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Sparse+Matrix+High&FORM=IRPRST&ck=4D6FF045CC5CF754D48F6841C068D9C2&selectedIndex=3&itb=0)
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