Recent results for column generation based diving heuristics

Math heuristics have become an essential component in mixed integer programming (MIP) solvers. As the Dantzig-Wolfe reformulation of a problem is typically tighter than that of the original compact formulation, heuristics based on rounding its linear program- ing (LP) solution can be more competitive. We focus on the so-called diving methods that used re-optimization after each LP rounding. Our numerical results on generalized assignment, cutting stock, and vertex coloring problems sets new benchmarks, highlighting the performance of diving heuristics as generic procedures in a column generation context and producing better solutions than state-of-the-art specialized heuristics in some cases.