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Large Scale Convex Optimization for System and Data Analysis

To be able to solve the semidefine programming Problems (SDP) involving large scale matrices in practice, one need to implement an efficient convex optimization algorithm [1]. Recently, a first-order augmented Lagrangian algorithm ALCC has been proposed in [2] to deal with regularized conic convex problems. We adapt this algorithm to solve SDPs resulted from convex formulation of system and data analysis problems. In the following, we briefly discuss the algorithm ALCC.