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More pathological examples for network flow problems

https://doi.org/10.1007/bf01580122
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf01580122openalex-snapshot
More pathological examples for network flow problems
inferredcites →https://doi.org/10.1057/jors.1970.122openalex-snapshot
Integer Programming and Network Flows.
inferredcites →https://doi.org/10.1145/321679.321693openalex-snapshot
Theoretical Efficiency of the Edmonds-Karp Algorithm for Computing Maximal Flows
inferredcites →https://doi.org/10.1145/321694.321699openalex-snapshot
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
inferredcites →https://doi.org/10.1287/mnsc.14.3.205openalex-snapshot
A Primal Method for Minimal Cost Flows with Applications to the Assignment and Transportation Problems
inferredcites →https://doi.org/10.2307/3008450openalex-snapshot
Integer Programming and Network Flows.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9780470400531.eorms0774openalex-snapshot
The Simplex Method and Its Complexity
inferred← citeshttps://doi.org/10.1002/nav.3800280308openalex-snapshot
Decomposition algorithms for minimal cut problems
inferred← citeshttps://doi.org/10.1002/net.3230230106openalex-snapshot
Canceling most helpful total cuts for minimum cost network flow
inferred← citeshttps://doi.org/10.1007/978-3-642-32278-5_6openalex-snapshot
Flows
inferred← citeshttps://doi.org/10.1007/978-3-662-04616-6_3openalex-snapshot
Deterministic Approaches
inferred← citeshttps://doi.org/10.1007/bf01240734openalex-snapshot
Tight bounds on the number of minimum-mean cycle cancellations and related results
inferred← citeshttps://doi.org/10.1007/bf01580132openalex-snapshot
A bad network problem for the simplex method and other minimum cost flow algorithms
inferred← citeshttps://doi.org/10.1007/bf01581046openalex-snapshot
Decomposition algorithms for locating minimal cuts in a network
inferred← citeshttps://doi.org/10.1007/bf01582232openalex-snapshot
Affirmative action algorithms
inferred← citeshttps://doi.org/10.1007/bf02288321openalex-snapshot
A computational comparison of the dinic and network simplex methods for maximum flow
inferred← citeshttps://doi.org/10.1007/bfb0120919openalex-snapshot
Minimal cost flows in regular matroids
inferred← citeshttps://doi.org/10.1016/0377-2217(82)90113-8openalex-snapshot
Weighted min cost flows
inferred← citeshttps://doi.org/10.1016/b978-0-444-88071-0.50015-1openalex-snapshot
Graph Algorithms
inferred← citeshttps://doi.org/10.1016/j.cor.2008.01.001openalex-snapshot
An exterior simplex type algorithm for the Minimum Cost Network Flow Problem
inferred← citeshttps://doi.org/10.1016/s0167-6377(00)00059-6openalex-snapshot
Minimum ratio canceling is oracle polynomial for linear programming, but not strongly polynomial, even for networks
inferred← citeshttps://doi.org/10.1016/s0927-0507(89)01005-4openalex-snapshot
Chapter IV Network flows
inferred← citeshttps://doi.org/10.1051/ro/2012015openalex-snapshot
On a dual network exterior point simplex type algorithm and its computational behavior
inferred← citeshttps://doi.org/10.1145/322033.322042openalex-snapshot
Flow Switching Approach to the Maximum Flow Problem: I
inferred← citeshttps://doi.org/10.1287/moor.12.4.718openalex-snapshot
The d-Step Conjecture and Its Relatives
inferred← citeshttps://doi.org/10.1287/moor.5.2.258openalex-snapshot
Theoretical Efficiency of the Algorithm “Capacity” for the Maximum Flow Problem
inferred← citeshttps://doi.org/10.15476/elte.2019.029openalex-snapshot
Efficient Algorithms for Graph Optimization Problems
inferred← citeshttps://doi.org/10.5555/127787.127812openalex-snapshot
Tight bounds on the number of minimum-mean cycle cancellations and related results
inferred← citeshttps://doi.org/10.5555/338219.338663openalex-snapshot
Minimum ratio canceling is oracle polynomial for linear programming, but not strongly polynomial, even for networks
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