1994
DOI: 10.1016/0305-0548(94)90035-3
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An incremental primal-dual method for generalized networks

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Cited by 5 publications
(7 citation statements)
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“…and values for π T are chosen to achieve dual feasibility. As shown in [9], the basic columns in A can be represented by a collection of trees, each tree rooted by a node in T. We denote such a tree with root node t ∈T by I(t).…”
Section: Problem Statement and Preliminariesmentioning
confidence: 99%
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“…and values for π T are chosen to achieve dual feasibility. As shown in [9], the basic columns in A can be represented by a collection of trees, each tree rooted by a node in T. We denote such a tree with root node t ∈T by I(t).…”
Section: Problem Statement and Preliminariesmentioning
confidence: 99%
“…No computer implementation ensued from their study. In this paper, it is demonstrated how steepest-edge directions are easily adapted into a particular simplex variant, the incremental primal -dual method [8,9]. Computational results indicate steepest-edge is uniformly superior to other selection criteria in the incremental primal -dual method, and the resulting code is more efficient than primal simplex on NETGEN problems.…”
Section: Introductionmentioning
confidence: 99%
“…Sun et al (1995) obtained that EMNET was between 11 to 17 times faster than a simplex based algorithm depending on the complexity of the network. Similarly to pure networks, primal-dual family algorithms are generally more efficient than simplex based algorithms (Curet 1994 and1998). In the case of using generalized network algorithms to solve pure networks, computation times are very similar in the case of primal-dual codes (Curet 1997 and1998).…”
Section: Comparisons Between Algorithmsmentioning
confidence: 87%
“…Most of the authors (Bersetkas 1985;Bersetkas &Tseng 1988 and1994;Kuczera 1993), agree about the superior performance of algorithms based on the relaxation method such as RELAX-IV and previous implementations. These algorithms usually perform faster by up to one order of magnitude than the other minimum cost flow problem algorithms.…”
Section: Solving the Minimum Cost Flow Problemmentioning
confidence: 99%
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