Approximation of barter exchanges with cycle length constraints

We explore the clearing problem in the barter exchange market. The problem, described in the terminology of graph theory, is to find a set of vertex-disjoint, length-restricted cycles that maximize the total weight in a weighted digraph. The problem has previously been shown to be NP-hard. We advance the understanding of this problem by the following contributions. We prove three constant inapproximability results for this problem. For the weighted graphs, we prove that it is NP-hard to approximate the clearing problem within a factor of 14/13 under general length constraints and within a factor of 434/433 when the cycle length is not longer than 3. For the unweighted graphs, we prove that this problem is NP-hard to approximate within a factor of 698/697. For the unweighted graphs when the cycle length is not longer than 3, we design and implement two simple and practical algorithms. Experiments on simulated data suggest that these algorithms yield excellent performances.

[1]  Richard M. Karp,et al.  Reducibility Among Combinatorial Problems , 1972, 50 Years of Integer Programming.

[2]  Alvin E. Roth,et al.  Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis , 1990 .

[3]  Carsten Lund,et al.  Proof verification and hardness of approximation problems , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.

[4]  Marek Karpinski,et al.  On Some Tighter Inapproximability Results, Further Improvements , 1998, Electron. Colloquium Comput. Complex..

[5]  P. Berman,et al.  On Some Tighter Inapproximability Results , 1998, Electron. Colloquium Comput. Complex..

[6]  Sanjeev Arora,et al.  Probabilistic checking of proofs: a new characterization of NP , 1998, JACM.

[7]  Stanislav J. Kirschbaum Slovakia , 1999, Concluding Observations of the UN Committee on the Elimination of Racial Discrimination.

[8]  Johan Håstad,et al.  Some optimal inapproximability results , 2001, JACM.

[9]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[10]  Marek Karpinski,et al.  Improved Approximation Lower Bounds on Small Occurrence Optimization , 2003, Electron. Colloquium Comput. Complex..

[11]  Alvin E. Roth,et al.  Pairwise Kidney Exchange , 2004, J. Econ. Theory.

[12]  Avrim Blum,et al.  Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges , 2007, EC '07.

[13]  Tuomas Sandholm,et al.  Online Stochastic Optimization in the Large: Application to Kidney Exchange , 2009, IJCAI.

[14]  Itai Ashlagi,et al.  Mix and match , 2010, EC '10.

[15]  Ariel D. Procaccia,et al.  Optimizing kidney exchange with transplant chains: theory and reality , 2012, AAMAS.

[16]  D. Gamarnik,et al.  The Need for (Long) Chains in Kidney Exchange , 2012 .

[17]  Ariel D. Procaccia,et al.  Dynamic Matching via Weighted Myopia with Application to Kidney Exchange , 2012, AAAI.

[18]  Ariel D. Procaccia,et al.  Failure-aware kidney exchange , 2013, EC '13.

[19]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[20]  Ariel D. Procaccia,et al.  Price of fairness in kidney exchange , 2014, AAMAS.

[21]  Jian Li,et al.  Egalitarian pairwise kidney exchange: fast algorithms vialinear programming and parametric flow , 2014, AAMAS.