Distributed asynchronous column generation

We propose a revision of the classical column generation algorithm for solving Dantzig-Wolfe decompositionsof mixed integer programs. It is meant to fully exploit the availability of distributed computing resources,making optimization algorithms in general purpose solvers to scale better.The main idea is to trigger massive parallelism by fully decoupling the computing flow of each component,including the resolution of the master problem, thus allowing different pricing algorithms to concurrently workon different sets of dual variables, and the master algorithm to asynchronously update dual information assoon as new columns are available.Our algorithms ensure the same optimality convergency properties of the classical method. Experiments onmixed integer programs for three benchmark problems from the combinatorial optimization literature proveour approach to be one order of magnitude faster than state-of-the-art general purpose solvers in computinghigh quality root node dual bounds. Even if devised to exploit clusters of machines which do not sharememory space, our algorithms show to be faster than earlier attempts from the literature also when run onvirtual machines hosted on a single physical one, proving this improvement to derive from our algorithmicmethodology rather than technological factors.