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  2. Minimax approximation algorithm - Wikipedia

    en.wikipedia.org/wiki/Minimax_approximation...

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  3. 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]

  4. Arg max - Wikipedia

    en.wikipedia.org/wiki/Arg_max

    In mathematics, the arguments of the maxima (abbreviated arg max or argmax) and arguments of the minima (abbreviated arg min or argmin) are the input points at which a function output value is maximized and minimized, respectively. [note 1] While the arguments are defined over the domain of a function, the output is part of its codomain.

  5. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  6. Maximum coverage problem - Wikipedia

    en.wikipedia.org/wiki/Maximum_coverage_problem

    The maximum coverage problem is a classical question in computer science, computational complexity theory, and operations research. It is a problem that is widely taught in approximation algorithms .

  7. Golden-section search - Wikipedia

    en.wikipedia.org/wiki/Golden-section_search

    The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.

  8. Gumbel distribution - Wikipedia

    en.wikipedia.org/wiki/Gumbel_distribution

    The standard Gumbel distribution is the case where = and = with cumulative distribution function = ()and probability density function = (+).In this case the mode is 0, the median is ⁡ (⁡ ()), the mean is (the Euler–Mascheroni constant), and the standard deviation is /

  9. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    In the language of tropical analysis, the softmax is a deformation or "quantization" of arg max and arg min, corresponding to using the log semiring instead of the max-plus semiring (respectively min-plus semiring), and recovering the arg max or arg min by taking the limit is called "tropicalization" or "dequantization".