Analysis of the Min-Sum Algorithm for Packing and Covering Problems via Linear Programming

Guy Even, Nissim Halabi

Research output: Contribution to journalArticlepeer-review


Message-passing algorithms based on belief-propagation (BP) are successfully used in many applications, including decoding error correcting codes and solving constraint satisfaction and inference problems. The BP-based algorithms operate over graph representations, called factor graphs, that are used to model the input. Although in many cases, the BP-based algorithms exhibit impressive empirical results, not much has been proved when the factor graphs have cycles. This paper deals with packing and covering integer programs in which the constraint matrix is zero-one, the constraint vector is integral, and the variables are subject to box constraints. We study the performance of the min-sum algorithm when applied to the corresponding factor graph models of packing and covering linear programmings (LPs). We compare the solutions computed by the min-sum algorithm for packing and covering problems to the optimal solutions of the corresponding LP relaxations. In particular, we prove that if the LP has an optimal fractional solution, then for each fractional component, the min-sum algorithm either computes multiple solutions or the solution oscillates below and above the fraction. This implies that the min-sum algorithm computes the optimal integral solution only if the LP has a unique optimal solution that is integral. The converse is not true in general. For a special case of packing and covering problems, we prove that if the LP has a unique optimal solution that is integral and on the boundary of the box constraints, then the min-sum algorithm computes the optimal solution in pseudopolynomial time. Our results unify and extend recent results for the maximum weight matching problem and for the maximum weight independent set problem.

Original languageEnglish
Article number7185433
Pages (from-to)5295-5305
Number of pages11
JournalIEEE Transactions on Information Theory
Issue number10
StatePublished - 1 Oct 2015


  • Graph cover
  • belief propagation (BP)
  • combinatorial optimization
  • covering problems
  • dynamic programming
  • factor graphs
  • linear programming (LP)
  • max-product algorithm
  • message-passing algorithms
  • min-sum algorithm
  • packing problems

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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