Convexly constrained linear inverse problems: iterative least-squares and regularization
A Sabharwal, LC Potter - IEEE Transactions on Signal …, 1998 - ieeexplore.ieee.org
IEEE Transactions on Signal Processing, 1998•ieeexplore.ieee.org
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.
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.
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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