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An iterative algorithm for the square-root Lasso
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AI
DOI：10.1088/1361-6420/ae2e38.png)
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Abstract
En 中文
In the framework of sparsity-enforcing regularisation for linear inverse problems, we consider the minimisation of a square-root Lasso cost function. To solve this problem we devise a simple modification of the iterative soft-thresholding algorithm (ISTA) for the Lasso problem and we prove convergence for this algorithm, which we call SQRT-ISTA. Under some additional assumptions, we derive an upper bound on the convergence rate of the cost function. We also generalise these results to the case of the group square-root Lasso, where sparsity is enforced for groups of variables instead of individual ones.
Keywords:
square-root Lasso
sparsity-enforcing regularisation
iterative soft-thresholding algorithm