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We investigate a parallel subspace correction framework for composite convex optimization.The variables are divided into blocks.At each iteration,the algorithms solve a subproblem on each block simultaneously,construct a search direction by combining their solutions on all blocks,then identify a new point along this direction.Their convergence is established under mild assumptions.Numerical results illustrate that our algorithms can run fast and return solutions no worse than those from the state-of-the-art algorithms.