Abstract. We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale problems even with dense matrix data. However, such methods are also known to converge quite slowly. In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. Initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of FISTA which is shown to be faster than ISTA by several orders of magnitude. Key words. iterative shrinkage-thresholding algorithm, deconvolution, linear inverse problem, least squares and l1 regularization problems, optimal gradient method, global rate of convergence, two-step iterative algorithms, image deblurring AMS subject classifications. 90C25, 90C06, 65F22
The newly convex optimization problem can be solved effectively using the fast iterative shrinkage thresholding algorithm (FISTA) REF .
M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems
SIAM Journal on Imaging Sciences
Mathematics Computer Science