Integer and combinatorial optimization
Integer and combinatorial optimization
An O(n2 log n) parallel max-flow algorithm
Journal of Algorithms
A data structure for dynamic trees
Journal of Computer and System Sciences
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Reducing the number of variables in integer and linear programming problems
Computational Optimization and Applications
Graph Algorithms
The Art of Computer Programming, Volume 1, Fascicle 1: MMIX -- A RISC Computer for the New Millennium (Art of Computer Programming)
A note on dominance relation in unbounded knapsack problems
Operations Research Letters
An efficient parallel algorithm for solving the Knapsack problem on hypercubes
Journal of Parallel and Distributed Computing
A new Karzanov-type O(n3) max-flow algorithm
Mathematical and Computer Modelling: An International Journal
A hybrid algorithm for the unbounded knapsack problem
Discrete Optimization
An overview of algorithms for network survivability
ISRN Communications and Networking
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Dinic has shown that the classic maximum flow problem on a graph of n vertices and m edges can be reduced to a sequence of at most n - 1 so-called 'blocking flow' problems on acyclic graphs. For dense graphs, the best time bound known for the blocking flow problems is O(n^2). Karzanov devised the first O(n^2)-time blocking flow algorithm, which unfortunately is rather complicated. Later Malhotra, Kumar and Maheshwari devise another O(n^2)-time algorithm, which is conceptually very simple but has some other drawbacks. In this paper we propose a simplification of Karzanov's algorithm that is easier to implement than Malhotra, Kumar and Maheshwari's method.