Efficient approximation of labeling problems with applications to immune repertoire analysis

Labeling problems are finding increasing applications to optimization problems. They usually get realized into linear or quadratic optimization problems, which are inefficient for large graphs. In this paper we propose an efficient primal-dual solution, ML
_PD
, for a family of labeling problems. We apply this algorithm to the analysis of immune repertoires, and compare it against our baseline approach based on refinement operators. We provide a comparative evaluation both in terms of accuracy and computational efficiency with respect to the baseline model, as well as to quadratic optimization.