document: An efficient algorithm for a class of fused lasso problems The fused Lasso penalty enforces sparsity in both the coefficients and their successive differences , which is desirable for applications with features ordered in some meaningful way . The resulting problem is , however , challenging to solve , as the fused Lasso penalty is both non-smooth and non-separable . Existing algorithms have high computational complexity and do not scale to large-size problems . In this paper , we propose an Efficient Fused Lasso Algorithm ( EFLA ) for optimizing this class of problems . One key building block in the proposed EFLA is the Fused Lasso Signal Approximator ( FLSA ) . To efficiently solve FLSA , we propose to reformulate it as the problem of finding an `` appropriate '' subgradient of the fused penalty at the minimizer , and develop a Subgradient Finding Algorithm ( SFA ) . We further design a restart technique to accelerate the convergence of SFA , by exploiting the special `` structures '' of both the original and the reformulated FLSA problems . Our empirical evaluations show that , both SFA and EFLA significantly outperform existing solvers . We also demonstrate several applications of the fused Lasso .
doc_bio_tags: O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O