What are the pros and cons of using LARS  versus using coordinate descent for fitting L1-regularized linear regression?
I am mainly interested in performance aspects (my problems tend to have
N in the hundreds of thousands and
p < 20.) However, any other insights would also be appreciated.
edit: Since I've posted the question, chl has kindly pointed out a paper  by Friedman et al where coordinate descent is shown to be considerably faster than other methods. If that's the case, should I as a practitioner simply forget about LARS in favour of coordinate descent?
 Efron, Bradley; Hastie, Trevor; Johnstone, Iain and Tibshirani, Robert (2004). "Least Angle Regression". Annals of Statistics 32 (2): pp. 407–499.
 Jerome H. Friedman, Trevor Hastie, Rob Tibshirani, "Regularization Paths for Generalized Linear Models via Coordinate Descent", Journal of Statistical Software, Vol. 33, Issue 1, Feb 2010.