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  2. Benders decomposition - Wikipedia

    en.wikipedia.org/wiki/Benders_decomposition

    Because the feasible space only shrinks as information is added, the objective value for the master function provides a lower bound on the objective function of the overall problem. Benders Decomposition is applicable to problems with a largely block-diagonal structure.

  3. Circuit complexity - Wikipedia

    en.wikipedia.org/wiki/Circuit_complexity

    Proving lower bounds on size of Boolean circuits computing explicit Boolean functions is a popular approach to separating complexity classes. For example, a prominent circuit class P/poly consists of Boolean functions computable by circuits of polynomial size.

  4. Vapnik–Chervonenkis dimension - Wikipedia

    en.wikipedia.org/wiki/Vapnik–Chervonenkis...

    In general, the VC dimension of a finite classification model, which can return at most different classifiers, is at most (this is an upper bound on the VC dimension; the Sauer–Shelah lemma gives a lower bound on the dimension).

  5. Fast Fourier transform - Wikipedia

    en.wikipedia.org/wiki/Fast_Fourier_transform

    A tight lower bound is not known on the number of required additions, although lower bounds have been proved under some restrictive assumptions on the algorithms. In 1973, Morgenstern [ 27 ] proved an Ω ( n log ⁡ n ) {\displaystyle \Omega (n\log n)} lower bound on the addition count for algorithms where the multiplicative constants have ...

  6. Cramér–Rao bound - Wikipedia

    en.wikipedia.org/wiki/Cramér–Rao_bound

    [6] [7] It is also known as Fréchet-Cramér–Rao or Fréchet-Darmois-Cramér-Rao lower bound. It states that the precision of any unbiased estimator is at most the Fisher information; or (equivalently) the reciprocal of the Fisher information is a lower bound on its variance.

  7. Convex hull algorithms - Wikipedia

    en.wikipedia.org/wiki/Convex_hull_algorithms

    The lower bound on worst-case running time of output-sensitive convex hull algorithms was established to be Ω(n log h) in the planar case. [1] There are several algorithms which attain this optimal time complexity. The earliest one was introduced by Kirkpatrick and Seidel in 1986 (who called it "the ultimate convex hull algorithm").

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  9. Circulation problem - Wikipedia

    en.wikipedia.org/wiki/Circulation_problem

    The circulation problem and its variants are a generalisation of network flow problems, with the added constraint of a lower bound on edge flows, and with flow conservation also being required for the source and sink (i.e. there are no special nodes). In variants of the problem, there are multiple commodities flowing through the network, and a ...