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On algorithms for solving least squares problems under an L1 penalty or an L1 constraint

Tibshirani (1996) proposed the least absolute shrinkage and selection operator (LASSO) which estimates a vector of regression coefficients by minimising the residual sum of squares subject to a constraint (penalty) on the sum of the absolute values of the coefficient estimates. In this paper, we describe several algorithms that can be used to calculate the LASSO solution.