2010 BalancedAllocationwithSuccinctR

• (Alaei et al., 2010) ⇒ Saeed Alaei, Ravi Kumar, Azarakhsh Malekian, and Erik Vee. (2010). “Balanced Allocation with Succinct Representation.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835872

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• http://scholar.google.com/scholar?q=%22Balanced+allocation+with+succinct+representation%22+2010
• http://portal.acm.org/citation.cfm?id=1835872&preflayout=flat#citedby

Quotes

Abstract

Motivated by applications in guaranteed delivery in computational advertising, we consider the general problem of balanced allocation in a bipartite supply-demand setting. Our formulation captures the notion of deviation from being balanced by a convex penalty function. While this formulation admits a convex programming solution, we strive for more robust and scalable algorithms.

For the case of [math]\displaystyle{ L_1 }[/math] penalty functions we obtain a simple combinatorial algorithm based on min-cost flow in graphs and show how to precompute a linear amount of information such that the allocation along any edge can be approximated in constant time. We then extend our combinatorial solution to any convex function by solving a convex cost flow. These scalable methods may have applications in other contexts stipulating balanced allocation.

We study the performance of our algorithms on large real-world graphs and show that they are efficient, scalable, and robust in practice.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 BalancedAllocationwithSuccinctR	Ravi Kumar Saeed Alaei Azarakhsh Malekian Erik Vee			Balanced Allocation with Succinct Representation		KDD-2010 Proceedings		10.1145/1835804.1835872		2010