  We introduce and asses several Divide \& Conquer heuristic strategies aimed
to solve large instances of the 0-1 Minimization Knapsack Problem. The method
subdivides a large problem in two smaller ones (or recursive iterations of the
same principle), to lower down the global computational complexity of the
original problem, at the expense of a moderate loss of quality in the solution.
Theoretical mathematical results are presented in order to guarantee an
algorithmically successful application of the method and to suggest the
potential strategies for its implementation. In contrast, due to the lack of
theoretical results, the solution's quality deterioration is measured
empirically by means of Monte Carlo simulations for several types and values of
the chosen strategies. Finally, introducing parameters of efficiency we suggest
the best strategies depending on the data input.
