Convexly constrained linear inverse problems: iterative least-squares and regularization

Abstract

| In this paper, we consider robust inversion of linear operators with convex constraints. We present an iteration that converges to the minimum norm least squares solution; a stopping rule is shown to regularize the constrained inversion. A constrained Laplace inversion is computed to illustrate the proposed algorithm. 
DOI: 10.1109/78.709518

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