Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm
نویسندگان
چکیده
منابع مشابه
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In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, Lq regularization with q ∈ (0, 1)) have been demonstrated to possess capability of gaining much benefit in sparsity-inducing and efficiency. As compared with the convex regularization approaches (say, L1 regularization), however, the convergence issue of the corresponding algorithms are more difficult t...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2016
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2016.2595499