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  2. Concave function - Wikipedia

    en.wikipedia.org/wiki/Concave_function

    The sum of two concave functions is itself concave and so is the pointwise minimum of two concave functions, i.e. the set of concave functions on a given domain form a semifield. Near a strict local maximum in the interior of the domain of a function, the function must be concave; as a partial converse, if the derivative of a strictly concave ...

  3. Convex set - Wikipedia

    en.wikipedia.org/wiki/Convex_set

    Equivalently, a convex set or a convex region is a set that intersects every line in a line segment, single point, or the empty set. [1] [2] For example, a solid cube is a convex set, but anything that is hollow or has an indent, for example, a crescent shape, is not convex. The boundary of a convex set in the plane is always a convex curve.

  4. Logarithmically concave function - Wikipedia

    en.wikipedia.org/wiki/Logarithmically_concave...

    The following are among the properties of log-concave distributions: If a density is log-concave, so is its cumulative distribution function (CDF). If a multivariate density is log-concave, so is the marginal density over any subset of variables. The sum of two independent log-concave random variables is log-concave. This follows from the fact ...

  5. Submodular set function - Wikipedia

    en.wikipedia.org/wiki/Submodular_set_function

    In mathematics, a submodular set function (also known as a submodular function) is a set function that, ... Then the concave closure is defined as + () ...

  6. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    For example, the problem of maximizing a concave function can be re-formulated equivalently as the problem of minimizing the convex function . The problem of maximizing a concave function over a convex set is commonly called a convex optimization problem. [8]

  7. Concave set - Wikipedia

    en.wikipedia.org/?title=Concave_set&redirect=no

    This page was last edited on 24 October 2014, at 14:23 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  8. Bauer maximum principle - Wikipedia

    en.wikipedia.org/wiki/Bauer_maximum_principle

    Any function that is concave and continuous, and defined on a set that is convex and compact, attains its minimum at some extreme point of that set. Since a linear function is simultaneously convex and concave, it satisfies both principles, i.e., it attains both its maximum and its minimum at extreme points.

  9. Convex measure - Wikipedia

    en.wikipedia.org/wiki/Convex_measure

    Thus, the collection of −∞-convex measures is the largest such class, whereas the 0-convex measures (the logarithmically concave measures) are the smallest class. The convexity of a measure μ on n-dimensional Euclidean space R n in the sense above is closely related to the convexity of its probability density function. [2]

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