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  2. Characterizations of the exponential function - Wikipedia

    en.wikipedia.org/wiki/Characterizations_of_the...

    Walter Rudin called it "the most important function in mathematics". [1] It is therefore useful to have multiple ways to define (or characterize) it. Each of the characterizations below may be more or less useful depending on context. The "product limit" characterization of the exponential function was discovered by Leonhard Euler. [2]

  3. Lp space - Wikipedia

    en.wikipedia.org/wiki/Lp_space

    An animated gif of p-norms 0.1 through 2 with a step of 0.05. This is not a norm because it is not homogeneous . For example, scaling the vector x {\displaystyle x} by a positive constant does not change the "norm".

  4. Infinity - Wikipedia

    en.wikipedia.org/wiki/Infinity

    [1] [3] For example, if a line is viewed as the set of all of its points, their infinite number (i.e., the cardinality of the line) is larger than the number of integers. [4] In this usage, infinity is a mathematical concept, and infinite mathematical objects can be studied, manipulated, and used just like any other mathematical object.

  5. Decompression practice - Wikipedia

    en.wikipedia.org/wiki/Decompression_practice

    This can lead to bubble formation and growth, with decompression sickness as a consequence. Partial pressure of oxygen is usually limited to 1.6 bar during in-water decompression for scuba divers, but can be up to 1.9 bar in-water and 2.2 bar in the chamber when using the US Navy tables for surface decompression. [98]

  6. Cut (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Cut_(graph_theory)

    The illustration on the right shows a maximum cut: the size of the cut is equal to 5, and there is no cut of size 6, or |E| (the number of edges), because the graph is not bipartite (there is an odd cycle). In general, finding a maximum cut is computationally hard. [3] The max-cut problem is one of Karp's 21 NP-complete problems. [4]

  7. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    The distribution of ⁠ X 1 + ⋯ + X n / √ n ⁠ need not be approximately normal (in fact, it can be uniform). [38] However, the distribution of c 1 X 1 + ⋯ + c n X n is close to (,) (in the total variation distance) for most vectors (c 1, ..., c n) according to the uniform distribution on the sphere c 2 1 + ⋯ + c 2 n = 1.