Convexly constrained linear inverse problems: iterativeleast-squares and regularization

Authors:
A. Sabharwal;L.C. Potter
Affiliations:
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH;-
Venue:
IEEE Transactions on Signal Processing
Year:
1998

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Abstract

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