@article{7fdb8af732ad4d32957239334ebe1509,
title = "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.",
author = "Ashutosh Sabharwal",
note = "Funding Information: Manuscript received August 28, 1996; revised January 21, 1998. This work was supported in part by the National Science Foundation under Grant MIP-9111044. The associate editor coordinating the review of this paper and approving it for publication was Dr. Victor E. DeBrunner. The authors are with the Department of Electrical Engineering, The Ohio State University, Columbus OH 43210 USA (e-mail:
[email protected]). Publisher Item Identifier S 1053-587X(98)05945-5.",
year = "1998",
doi = "10.1109/78.709518",
language = "English (US)",
volume = "46",
pages = "2345--2352",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "9",
}