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Graver basis and proximity techniques for block-structured separable convex integer minimization problems

Raymond Hemmecke(hemmecke***at***ma.tum.de)
Matthias Köppe(mkoeppe***at***math.ucdavis.edu)
Robert Weismantel(robert.weismantel***at***ifor.math.ethz.ch)

Abstract: We consider N-fold 4-block decomposable integer programs, which simultaneously generalize N-fold integer programs and two-stage stochastic integer programs with N scenarios. In previous work [R. Hemmecke, M. Koeppe, R. Weismantel, A polynomial-time algorithm for optimizing over N-fold 4-block decomposable integer programs, Proc. IPCO 2010, Lecture Notes in Computer Science, vol. 6080, Springer, 2010, pp. 219--229], it was proved that for fixed blocks but variable N, these integer programs are polynomial-time solvable for any linear objective. We extend this result to the minimization of separable convex objective functions. Our algorithm combines Graver basis techniques with a proximity result [D.S. Hochbaum and J.G. Shanthikumar, Convex separable optimization is not much harder than linear optimization, J. ACM 37 (1990), 843--862], which allows us to use convex continuous optimization as a subroutine.

Keywords: N-fold integer programs, Graver basis, augmentation algorithm, proximity, polynomial-time algorithm, stochastic multi-commodity flow, stochastic integer programming

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation:

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Entry Submitted: 07/04/2012
Entry Accepted: 07/05/2012
Entry Last Modified: 07/04/2012

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