Polynomial Dual Network Simplex Algorithms

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Polynomial Dual Network Simplex Algorithms
James B Orlin
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To update d{v) for the remaining bad nodes we have to consider ail edges leaving the nodes that became good during this pivot. We shall maintain d{v) in a Fibonacci heap. Overall, there are at most m updates (one for every arc). The total time spent over all iterations of Make-Good on searching for nodes that become good and for the new w' is bounded by 0{n). The claim follows since updating and finding the minimum can be done in 0{m + n log n) time. I 3. 2 A polynomial dual simplex algorithm.
...We are now prepared to describe the polynomial time dual simplex algorithm. The algorithm finds an optimal solution for the minimum cost flow problem starting with the shortest path tree T directed from node r. In the Ccise that all arcs are non-negative, one can find T in 0{n) pivots [4]; in case the costs may be negative, one can find T^ in 0{n^) pivots using the dual simplex algorithm of Balinski [2]. We assume that T° is obtained using an appropriate subroutine.
In our algorithm, we will keep track of a scahng parameter A that is nondecrejising from iteration to iteration.


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