A network simplex algorithm with O(n) consecutive degenerate pivots
Introduction
The primal simplex algorithm for the minimum cost flow problem, known as the network simplex algorithm, is known to exhibit cycling (that is, an indefinitely repeating sequence of degenerate pivots). Cunningham [2], [3] showed that by using specific types of basis, known as the strongly feasible basis, cycling can be avoided. This technique (described in Section 2) starts with a strongly feasible basis, allows any nonbasic arc to enter the basis, but requires that if several arcs qualify as leaving arcs then an arc satisfying a particular property be chosen as the leaving arc. Cunningham [3], however, showed through a counter-example that the network simplex algorithm maintaining strongly feasible basis can still perform an exponentially long sequence of degenerate pivots, a phenomenon known as stalling.
Several anti-stalling pivot rules have been proposed by researchers. Cunningham [3] and Roohey-Laleh [7] proposed several anti-stalling pivot rules with bounds of O(m) and O(n2) on the number of consecutive degenerate pivots, where n is the number of nodes and m is the number of arcs in the network. Goldfarb et al. [5] also proposed several anti-stalling pivot rules, one of which performs at most k(k+1)/2 consecutive degenerate pivots, where k is the number of degenerate arcs in the basis. (Observe that k⩽n.)
In this paper, we describe an anti-stalling pivot rule that ensures that the network simplex algorithm performs at most k degenerate pivots. This rule maintains a strongly feasible basis and uses a negative cost augmenting cycle to identify a sequence of entering variables that prevent stalling.
We point out that the results in this paper do not (to the best of our knowledge) lead to a polynomial time network simplex algorithm for the minimum cost flow problem. Currently, the only known polynomial time network primal simplex algorithm is due to Orlin [6] which performs pivots.
Section snippets
Network simplex algorithm for the minimum cost flow problem
In this section, we first present some basic definitions and notation. We then give the linear programming formulation of the minimum cost flow problem and describe the network simplex algorithm. We refer the reader to Ahuja et al. [1, Chapter 11], for a review of the network simplex algorithm.
Let G=(N,A) be a directed network (or graph) defined by a set N of n nodes and a set A of m directed arcs. For the simplicity of notation, we assume that there are no parallel arcs. Each arc (i,j)∈A has
Augmenting and valid cycles
Let C be a cycle in G. We associate an orientation to the cycle C. Let denote the set of forward arcs (that is, along the orientation of the cycle) and denote the set of backward arcs in C (that is, opposite to the orientation of the cycle). A cycle C is an augmenting cycle in G with respect to a flow x if xij<uij for each arc , and xij>0 for each arc . We define the cost of an augmenting cycle , as the change it will cause to the total flow cost if unit flow is
The anti-stalling pivot rule
We will now present our pivot rule. Our rule uses a valid cycle to identify entering arcs and uses Lemma 2 to identify leaving arcs.
Anti-Stalling Pivot Rule:
Theorem 1 The network simplex algorithm using the anti-stalling pivot rule performs at most k there is an eligible arc such that nodes and are in the same free component, pivot in the arc ; find a valid negative cycle in with respect to the flow ; is valid select an eligible arc in and pivot it in; ;
Acknowledgments
We thank the referee and the Associate Editor for their perceptive comments and helpful suggestions. The research of the first author was supported by the NSF Grants DMI-9900087 and DMI-0085682. The second author was supported by the NSF Grants DMI-9810359 and DMI-9820998.
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