Sentence examples for maximum flow algorithm from inspiring English sources

For the calculation of the maximum flow we used an adaptation of Cherkassky and Goldberg's maximum flow algorithm [8].

According to the max-flow min-cut theorem (Papadimitriou and Steiglitz, 1982), a minimum s/ t-cut can be efficiently computed with the maximum flow algorithm (Goldberg and Tarjan, 1988).

This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation.

We show how the optimization problem can be solved using a minimum-cost maximum-flow algorithm, and propose simple extensions that allow for the explicit incorporation of additional, variable, cell division events during late development.

Here, this is the case since violated cut constraints can be detected by a maximum-flow algorithm in a support graph with arc-capacities given by the current linear programming solution.

When solving the latter formulation, at most q augmentations are required to find the optimal solution using the simple minimum-cost maximum-flow algorithm suggested by Edmonds and Karp (Edmonds, 1972), giving an asymptotic runtime of.

By reducing the computational complexity in using a minimum-cost maximum-flow algorithm, we make feasible a cross-entropy based learning algorithm to tune the weights of the features in our scross-entropy based ulearningy tralgorithme accurato model tune is capable of handling the variable cell divisions.

The paper presents a Minimum Cost Maximum Flow (MCMF) algorithm and compares it against a modified Bin-Packing formulation; the MCMF algorithm exhibits very good performance and scalability properties.

MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities.

Having the same limitation in using the out-of-date phenotype similarity network as in [ 19], study [ 20] prioritized protein complexes implicated in human diseases using a maximum information flow algorithm on a heterogeneous network which was constructed by combining a protein interaction network and the phenotypic similarity network.

This step models the anchor graph as a flow network and uses a maximum flow, minimum cut algorithm (Ford and Fulkerson, 1956) to find bottlenecks in the graph that are used to partition connected components that violate criteria for LCBs.