  In this paper, we consider the problem of minimizing the sum of two convex
functions subject to linear linking constraints. The classical alternating
direction type methods usually assume that the two convex functions have
relatively easy proximal mappings. However, many problems arising from
statistics, image processing and other fields have the structure that while one
of the two functions has easy proximal mapping, the other function is smoothly
convex but does not have an easy proximal mapping. Therefore, the classical
alternating direction methods cannot be applied. To deal with the difficulty,
we propose in this paper an alternating direction method based on
extragradients. Under the assumption that the smooth function has a Lipschitz
continuous gradient, we prove that the proposed method returns an
$\epsilon$-optimal solution within $O(1/\epsilon)$ iterations. We apply the
proposed method to solve a new statistical model called fused logistic
regression. Our numerical experiments show that the proposed method performs
very well when solving the test problems. We also test the performance of the
proposed method through solving the lasso problem arising from statistics and
compare the result with several existing efficient solvers for this problem;
the results are very encouraging indeed.
