  We present a path algorithm for the generalized lasso problem. This problem
penalizes the $\ell_1$ norm of a matrix D times the coefficient vector, and has
a wide range of applications, dictated by the choice of D. Our algorithm is
based on solving the dual of the generalized lasso, which greatly facilitates
computation of the path. For $D=I$ (the usual lasso), we draw a connection
between our approach and the well-known LARS algorithm. For an arbitrary D, we
derive an unbiased estimate of the degrees of freedom of the generalized lasso
fit. This estimate turns out to be quite intuitive in many applications.
