PEIP (version 1.7)

irlsl1reg: L1 least squares with sparsity

Description

Solves the system Gm=d using sparsity regularization on Lm. Solves the L1 regularized least squares problem: min norm(G*m-d,2)^2+alpha*norm(L*m,1)

Usage

irlsl1reg(G, d, L, alpha, maxiter = 100, tolx = 1e-04, tolr = 1e-06)

Arguments

G

design matrix

d

right hand side

L

regularization matrix

alpha

regularization parameter

maxiter

Maximum number of IRLS iterations

tolx

Tolerance on successive iterates

tolr

Tolerance below which we consider an element of L*m to be effectively zero

Value

• m
  model vector

References

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