PEIP (version 1.7)

cgls: Conjugate gradient Least squares

Description

Conjugate gradient Least squares

Usage

cgls(Gmat, dee, niter)

Arguments

Gmat

input matrix

dee

right hand side

niter

max number of iterations

Value

• X
  matrix of models
• rho
  misfit norms
• eta
  model norms

Details

Performs niter iterations of the CGLS algorithm on the least squares problem min norm(G*m-d). Gmat should be a sparse matrix.

References

Aster, R.C., C.H. Thurber, and B. Borchers, Parameter Estimation and Inverse Problems, Elsevier Academic Press, Amsterdam, 2005.