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We\nadopt the alternating direction method of multipliers and\nconvert the original optimization problem into a sequence of\nL1-penalized least-squares minimization problems that are\nefficiently solved by linearization and multi-stage screening.\nIn addition to sparse linear model estimation, we provide\nextensions of these methods to sparse Gaussian graphical model\nestimation, including TIGER and CLIME, using either L1 or\nadaptive penalties. Missing values can be tolerated for Dantzig\nselector and CLIME. Computation is memory-optimized using\nsparse matrix output. 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