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  2. Network flow problem - Wikipedia

    en.wikipedia.org/wiki/Network_flow_problem

    In combinatorial optimization, network flow problems are a class of computational problems in which the input is a flow network (a graph with numerical capacities on its edges), and the goal is to construct a flow, numerical values on each edge that respect the capacity constraints and that have incoming flow equal to outgoing flow at all vertices except for certain designated terminals.

  3. Multi-commodity flow problem - Wikipedia

    en.wikipedia.org/wiki/Multi-commodity_flow_problem

    The problem can be solved e.g. by minimizing . A common linearization of this problem is the minimization of the maximum utilization , where. In the minimum cost multi-commodity flow problem, there is a cost for sending a flow on . You then need to minimize. In the maximum multi-commodity flow problem, the demand of each commodity is not fixed ...

  4. Minimum-cost flow problem - Wikipedia

    en.wikipedia.org/wiki/Minimum-cost_flow_problem

    The minimum-cost flow problem (MCFP) is an optimization and decision problem to find the cheapest possible way of sending a certain amount of flow through a flow network.A typical application of this problem involves finding the best delivery route from a factory to a warehouse where the road network has some capacity and cost associated.

  5. Flow network - Wikipedia

    en.wikipedia.org/wiki/Flow_network

    Flow network. In graph theory, a flow network (also known as a transportation network) is a directed graph where each edge has a capacity and each edge receives a flow. The amount of flow on an edge cannot exceed the capacity of the edge. Often in operations research, a directed graph is called a network, the vertices are called nodes and the ...

  6. Maximum flow problem - Wikipedia

    en.wikipedia.org/wiki/Maximum_flow_problem

    The maximum flow problem can be seen as a special case of more complex network flow problems, such as the circulation problem. The maximum value of an s-t flow (i.e., flow from source s to sink t) is equal to the minimum capacity of an s-t cut (i.e., cut severing s from t) in the network, as stated in the max-flow min-cut theorem .

  7. Disjoint-set data structure - Wikipedia

    en.wikipedia.org/wiki/Disjoint-set_data_structure

    O(n)[1] In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that stores a collection of disjoint (non-overlapping) sets. Equivalently, it stores a partition of a set into disjoint subsets. It provides operations for adding new sets, merging sets (replacing them ...

  8. Max-flow min-cut theorem - Wikipedia

    en.wikipedia.org/wiki/Max-flow_min-cut_theorem

    The other half of the max-flow min-cut theorem refers to a different aspect of a network: the collection of cuts. An s-t cut C = (S, T) is a partition of V such that s ∈ S and t ∈ T. That is, an s - t cut is a division of the vertices of the network into two parts, with the source in one part and the sink in the other.

  9. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    Network flows [6] are a fundamental concept in graph theory and operations research, often used to model problems involving the transportation of goods, liquids, or information through a network. A network flow problem typically involves a directed graph where each edge represents a pipe, wire, or road, and each edge has a capacity, which is ...