A strongly polynomial minimum cost circulation algorithm
Combinatorica
Network generation using the Prufer code
Computers and Operations Research
Solving minimum-cost flow problems by successive approximation
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Use of dynamic trees in a network simplex algorithm for the maximum flow problem
Mathematical Programming: Series A and B
An infeasible (exterior point) simplex algorithm for assignment problems
Mathematical Programming: Series A and B
Finding minimum-cost flows by double scaling
Mathematical Programming: Series A and B
A faster strongly polynomial minimum cost flow algorithm
Operations Research
Interior point algorithms for network flow problems
Advances in linear and integer programming
Applying steepest-edge techniques to a network primal-dual algorithm
Computers and Operations Research
A polynomial time primal network simplex algorithm for minimum cost flows
Mathematical Programming: Series A and B
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
On using exterior penalty approaches for solving linear programming problems
Computers and Operations Research
Network Models in Optimization and Their Applications in Practice
Network Models in Optimization and Their Applications in Practice
Linear Programming and Network Flows
Linear Programming and Network Flows
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In this paper a new Network Exterior Point Simplex Algorithm (NEPSA) for the Minimum Cost Network Flow Problem (MCNFP) is analytically presented. NEPSA belongs to a special simplex type category and is a modification of the classical network simplex algorithm. The main idea of the algorithm is to compute two flows. One flow is basic but not always feasible and the other is feasible but not always basic. A complete proof of correctness for the proposed algorithm is also presented. Moreover, the computational behavior of NEPSA is shown by an empirical study carried out for randomly generated sparse instances created by the well-known GRIDGEN network problem generator.