A parallel approximation algorithm for positive linear programming
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Global Optimization Using Local Information with Applications to Flow Control
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Sequential and Parallel Algorithms for Mixed Packing and Covering
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Distributed network monitoring and multicommodity flows: a primal-dual approach
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Distributed algorithms for multicommodity flow problems via approximate steepest descent framework
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
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We consider the Active Min-Cost Measurement problem to minimize the cost incurred by measuring network link delays. Although the problem has a polynomial representation, its covering LP formulation, for which most of the previous distributed algorithms apply, has an exponential number of variables, one for each path. We present first known distributed (1+ε) approximation algorithm for this problem that converges in time that is linear in the maximal path length and poly-logarithmic in the size of the entire network and has polynomial computational overhead. Previous distributed solutions achieving similar approximations required either convergence time that is polynomial or computational overhead that is exponential in the size of the entire network.