Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Introduction to algorithms
Improving the location of minisum facilities through network modification
Annals of Operations Research - Special issue on locational decisions
The network inhibition problem
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Improved Algorithms for Bipartite Network Flow
SIAM Journal on Computing
Modifying edges of a network to obtain short subgraphs
Theoretical Computer Science - Special issue: graph theoretic concepts in computer science
Increasing the weight of minimum spanning trees
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Efficient algorithms for robustness in matroid optimization
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Mathematical Techniques for Efficient Record Segmentation in Large Shared Databases
Journal of the ACM (JACM)
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
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The robustness function of an optimization (minimization) problem measures the maximum increase in the value of its optimal solution that can be produced by spending a given amount of resources increasing the values of the elements in its input. We present efficient algorithms for computing the robustness function of resource allocation and scheduling problems that can be modeled with partition and scheduling matroids. For the case of scheduling matroids, we give an O(m2n2) time algorithm for computing a complete description of the robustness function, where m is the number of elements in the matroid and n is its rank. For partition matroids, we give two algorithms: one that computes the complete robustness function in O(m log m) time, and other that optimally evaluates the robustness function at only a specified point.