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  2. Push–relabel maximum flow algorithm - Wikipedia

    en.wikipedia.org/wiki/Push–relabel_maximum_flow...

    Residual Graph Initialise the residual graph by setting the preflow to values 0 and initialising the labeling. Initial saturating push is performed across all preflow arcs out of the source, s. Node a is relabeled in order to push its excess flow towards the sink, t.

  3. Restricted maximum likelihood - Wikipedia

    en.wikipedia.org/wiki/Restricted_maximum_likelihood

    In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that nuisance parameters have no effect.

  4. Maximum flow problem - Wikipedia

    en.wikipedia.org/wiki/Maximum_flow_problem

    The algorithm runs while there is a vertex with positive excess, i.e. an active vertex in the graph. The push operation increases the flow on a residual edge, and a height function on the vertices controls through which residual edges can flow be pushed. The height function is changed by the relabel operation.

  5. Ford–Fulkerson algorithm - Wikipedia

    en.wikipedia.org/wiki/Ford–Fulkerson_algorithm

    The Ford–Fulkerson method or Ford–Fulkerson algorithm (FFA) is a greedy algorithm that computes the maximum flow in a flow network.It is sometimes called a "method" instead of an "algorithm" as the approach to finding augmenting paths in a residual graph is not fully specified [1] or it is specified in several implementations with different running times. [2]

  6. Flow network - Wikipedia

    en.wikipedia.org/wiki/Flow_network

    The residual capacity of an arc e with respect to a pseudo-flow f is denoted c f, and it is the difference between the arc's capacity and its flow. That is, c f ( e ) = c ( e ) - f ( e ) . From this we can construct a residual network , denoted G f ( V , E f ) , with a capacity function c f which models the amount of available capacity on the ...

  7. Normal probability plot - Wikipedia

    en.wikipedia.org/wiki/Normal_probability_plot

    Normal probability plots are made of raw data, residuals from model fits, and estimated parameters. A normal probability plot. In a normal probability plot (also called a "normal plot"), the sorted data are plotted vs. values selected to make the resulting image look close to a straight line if the data are approximately normally distributed.

  8. Departure function - Wikipedia

    en.wikipedia.org/wiki/Departure_function

    The Peng–Robinson equation of state relates the three interdependent state properties pressure P, temperature T, and molar volume V m.From the state properties (P, V m, T), one may compute the departure function for enthalpy per mole (denoted h) and entropy per mole (s): [2]

  9. Max-flow min-cut theorem - Wikipedia

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

    a finite directed graph G = (V, E), where V denotes the finite set of vertices and E ⊆ V×V is the set of directed edges; a source s ∈ V and a sink t ∈ V; a capacity function, which is a mapping : + denoted by c uv or c(u, v) for (u,v) ∈ E. It represents the maximum amount of flow that can pass through an edge.