Title :
Convexly constrained linear inverse problems: iterative least-squares and regularization
Author :
Sabharwal, Ashutosh ; Potter, Lee C.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
fDate :
9/1/1998 12:00:00 AM
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
Keywords :
Laplace transforms; inverse problems; iterative methods; least squares approximations; signal reconstruction; signal restoration; Laplace transformation; constrained Laplace inversion; convergence; convexly constrained linear inverse problems; image reconstruction; iterative least-squares; linear operators; minimum norm least squares; noise power; regularization; robust inversion; signal reconstruction; signal restoration; stopping rule; Energy measurement; Extrapolation; Image reconstruction; Inverse problems; Least squares methods; Noise measurement; Robustness; Signal restoration; Subspace constraints; Vectors;
Journal_Title :
Signal Processing, IEEE Transactions on
