Sparse Frisch-Newton Algorithms for Quantile Regression

Several recent developments in quantile regression are crucially dependent on sparse linear algebra to accelerate computations. In two papers Pin Ng and I describe modifications of the Frisch-Newton interior point methods proposed in an earlier Statistical Science paper with Steve Portnoy, that exploit sparse structure in the design matrix of the QR problem.
Inequality Constrained Quantile Regression,
A Sparse Frisch-Newton Algorithm for Quantile Regression,
Experimental R code for the Candes and Tao Dantzig selector is available here,