Speeding Up the Estimation of Expected Maximum Flows Through Reliable Networks
AbstractIn this paper we present a strategy for speeding up the estimation of expected maximum flows through reliable networks. Our strategy tries to minimize the repetition of computational effort while evaluating network states sampled using the crude Monte Carlo method. Computational experiments with this strategy on three types of randomly generated networks show that it reduces the number of flow augmentations required for evaluating the states in the sample by as much as 52% on average with a standard deviation of 7% compared to the conventional strategy. This leads to an average time saving of about 71% with a standard deviation of about 8%. [W.P. No. 2009-04-05]
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Bibliographic InfoPaper provided by eSocialSciences in its series Working Papers with number id:2698.
Date of creation: Jul 2010
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Network Flows; Reliable Networks; Cold Start; Warm Start; Reliable Network Evaluation Strategy;
Other versions of this item:
- Megha Sharma & Ghosh, Diptesh, . "Speeding Up the Estimation of Expected Maximum Flows Through Reliable Networks," IIMA Working Papers WP2009-04-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
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- Sharma, Megha & Ghosh, Diptesh, . "Computing the probability mass function of the maximum flow through a reliable network," IIMA Working Papers WP2009-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
- Sharma, Megha & Ghosh, Diptesh, . "Evaluating Downside Risks in Reliable Networks," IIMA Working Papers WP2009-09-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
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