TY - GEN
T1 - A comparison of the computational performance of Iteratively Reweighted Least Squares and alternating minimization algorithms for ℓ1 inverse problems
AU - Rodríguez, Paul
AU - Wohlberg, Brendt
PY - 2012
Y1 - 2012
N2 - Alternating minimization algorithms with a shrinkage step, derived within the Split Bregman (SB) or Alternating Direction Method of Multipliers (ADMM) frameworks, have become very popular for ℓ1-regularized problems, including Total Variation and Basis Pursuit Denoising. It appears to be generally assumed that they deliver much better computational performance than older methods such as Iteratively Reweighted Least Squares (IRLS). We show, however, that IRLS type methods are computationally competitive with SB/ADMM methods for a variety of problems, and in some cases outperform them.
AB - Alternating minimization algorithms with a shrinkage step, derived within the Split Bregman (SB) or Alternating Direction Method of Multipliers (ADMM) frameworks, have become very popular for ℓ1-regularized problems, including Total Variation and Basis Pursuit Denoising. It appears to be generally assumed that they deliver much better computational performance than older methods such as Iteratively Reweighted Least Squares (IRLS). We show, however, that IRLS type methods are computationally competitive with SB/ADMM methods for a variety of problems, and in some cases outperform them.
KW - Inverse Problems
KW - Iteratively Reweighted Least Squares
KW - Split-Bregman
KW - Total Variation
UR - http://www.scopus.com/inward/record.url?scp=84875829517&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6467548
DO - 10.1109/ICIP.2012.6467548
M3 - Conference contribution
AN - SCOPUS:84875829517
SN - 9781467325332
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3069
EP - 3072
BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012
Y2 - 30 September 2012 through 3 October 2012
ER -