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Microsoft
Machine Learning Work Shop-Session 5 – Lin Xiao – “A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem”
We consider the l1-regularized least-squares problem in the context of sparse recovery or compressed sensing. The standard proximal gradient method (iterative soft-thresholding) has low computational cost per iteration but a rather slow convergence rate. Nevertheless, when the solution is sparse, it often exhibits fast linear convergence in the ...
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