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  2. Ordination (statistics) - Wikipedia

    en.wikipedia.org/wiki/Ordination_(statistics)

    Ordination or gradient analysis, in multivariate analysis, is a method complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing). In contrast to cluster analysis, ordination orders quantities in a (usually lower-dimensional) latent space. In the ordination space, quantities that are near ...

  3. Analyse-it - Wikipedia

    en.wikipedia.org/wiki/Analyse-it

    Analyse-it is a statistical analysis add-in for Microsoft Excel. Analyse-it is the successor to Astute, developed in 1992 for Excel 4 and the first statistical analysis add-in for Microsoft Excel. Analyse-it is the successor to Astute, developed in 1992 for Excel 4 and the first statistical analysis add-in for Microsoft Excel.

  4. Gradient method - Wikipedia

    en.wikipedia.org/wiki/Gradient_method

    In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)} with the search directions defined by the gradient of the function at the current point.

  5. Barzilai-Borwein method - Wikipedia

    en.wikipedia.org/wiki/Barzilai-Borwein_method

    The Barzilai-Borwein method [1] is an iterative gradient descent method for unconstrained optimization using either of two step sizes derived from the linear trend of the most recent two iterates. This method, and modifications, are globally convergent under mild conditions, [ 2 ] [ 3 ] and perform competitively with conjugate gradient methods ...

  6. ADMB - Wikipedia

    en.wikipedia.org/wiki/ADMB

    ADMB or AD Model Builder is a free and open source software suite for non-linear statistical modeling. [3] [4] It was created by David Fournier and now being developed by the ADMB Project, a creation of the non-profit ADMB Foundation.

  7. Adjoint state method - Wikipedia

    en.wikipedia.org/wiki/Adjoint_state_method

    The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks. [2] The adjoint state space is chosen to simplify the physical interpretation of equation ...

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