Algorithms for Fitting the Constrained Lasso

• Published in: Journal of computational and graphical statistics Vol. 27; no. 4; pp. 861 - 871
• Main Authors: Gaines, Brian R., Kim, Juhyun, Zhou, Hua
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 01.01.2018; American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America
• Subjects: Algorithms; Alternating direction method of multipliers; Convex optimization; Generalized lasso; Linear constraints; Penalized regression; Regularization path; Generalized lasso; Alternating direction method of multipliers; Penalized regression; Convex optimization; Regularization path; Linear constraints
• ISSN: 1061-8600; 1537-2715
• DOI: 10.1080/10618600.2018.1473777

We compare alternative computing strategies for solving the constrained lasso problem. As its name suggests, the constrained lasso extends the widely used lasso to handle linear constraints, which allow the user to incorporate prior information into the model. In addition to quadratic programming, we employ the alternating direction method of multipliers (ADMM) and also derive an efficient solution path algorithm. Through both simulations and benchmark data examples, we compare the different algorithms and provide practical recommendations in terms of efficiency and accuracy for various sizes of data. We also show that, for an arbitrary penalty matrix, the generalized lasso can be transformed to a constrained lasso, while the converse is not true. Thus, our methods can also be used for estimating a generalized lasso, which has wide-ranging applications. Code for implementing the algorithms is freely available in both the Matlab toolbox SparseReg and the Julia package ConstrainedLasso . Supplementary materials for this article are available online.