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  2. EXPSPACE - Wikipedia

    en.wikipedia.org/wiki/EXPSPACE

    An example of an EXPSPACE-complete problem is the problem of recognizing whether two regular expressions represent different languages, where the expressions are limited to four operators: union, concatenation, the Kleene star (zero or more copies of an expression), and squaring (two copies of an expression).

  3. Matrix exponential - Wikipedia

    en.wikipedia.org/wiki/Matrix_exponential

    The matrix exponential satisfies the following properties. [2] We begin with the properties that are immediate consequences of the definition as a power series: e 0 = I; exp(X T) = (exp X) T, where X T denotes the transpose of X. exp(X ∗) = (exp X) ∗, where X ∗ denotes the conjugate transpose of X. If Y is invertible then e YXY −1 = Ye ...

  4. NumPy - Wikipedia

    en.wikipedia.org/wiki/NumPy

    NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]

  5. Jordan normal form - Wikipedia

    en.wikipedia.org/wiki/Jordan_normal_form

    For example, in principle the Jordan form could give a closed-form expression for the exponential exp(A). The number of Jordan blocks corresponding to λ i of size at least j is dim ker(A − λ i I) j − dim ker(A − λ i I) j−1. Thus, the number of Jordan blocks of size j is

  6. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    Examples are the simple gravitation law connecting masses and distance with the resulting force, or the formula for equilibrium concentrations of chemicals in a solution that connects concentrations of educts and products. Assuming log-normal distributions of the variables involved leads to consistent models in these cases.

  7. LogSumExp - Wikipedia

    en.wikipedia.org/wiki/LogSumExp

    Other than this direction, it is strictly convex (the Hessian has rank ⁠ ⁠), so for example restricting to a hyperplane that is transverse to the diagonal results in a strictly convex function. See L S E 0 + {\displaystyle \mathrm {LSE} _{0}^{+}} , below.

  8. Laplace distribution - Wikipedia

    en.wikipedia.org/wiki/Laplace_distribution

    In probability theory and statistics, the Laplace distribution is a continuous probability distribution named after Pierre-Simon Laplace.It is also sometimes called the double exponential distribution, because it can be thought of as two exponential distributions (with an additional location parameter) spliced together along the abscissa, although the term is also sometimes used to refer to ...

  9. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    The softmax function, also known as softargmax [1]: 184 or normalized exponential function, [2]: 198 converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression .