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

Ashutosh Sabharwal

doi:10.1109/78.709518

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

Ashutosh Sabharwal

Research output: Contribution to journal › Article › peer-review

64 Scopus citations

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.

Original language	English (US)
Pages (from-to)	2345-2352
Number of pages	8
Journal	IEEE Transactions on Signal Processing
Volume	46
Issue number	9
DOIs	https://doi.org/10.1109/78.709518
State	Published - 1998

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1109/78.709518

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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.",

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PY - 1998

Y1 - 1998

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AB - 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.

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

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