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Accelerated projected gradient method for linear inverse problems with sparsity constraints. (English) Zbl 1175.65062
Summary: Regularization of ill-posed linear inverse problems via \(\ell _{1}\) penalization has been proposed for cases where the solution is known to be (almost) sparse. One way to obtain the minimizer of such an \(\ell _{1}\) penalized functional is via an iterative soft-thresholding algorithm. We propose an alternative implementation to \(\ell _{1}\)-constraints, using a gradient method, with projection on \(\ell _{1}\)-balls. The corresponding algorithm uses again iterative soft-thresholding, now with a variable thresholding parameter. We also propose accelerated versions of this iterative method, using ingredients of the (linear) steepest descent method. We prove convergence in norm for one of these projected gradient methods, without and with acceleration.

MSC:
65J10 Numerical solutions to equations with linear operators (do not use 65Fxx)
65J20 Numerical solutions of ill-posed problems in abstract spaces; regularization
65J22 Numerical solution to inverse problems in abstract spaces
47A52 Linear operators and ill-posed problems, regularization
15A29 Inverse problems in linear algebra
49M30 Other numerical methods in calculus of variations (MSC2010)
65F22 Ill-posedness and regularization problems in numerical linear algebra
65K10 Numerical optimization and variational techniques
90C25 Convex programming
52A41 Convex functions and convex programs in convex geometry
Software:
Mathematica
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References:
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