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2. 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 ...

3. 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 ...

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. Analyse-it - Wikipedia

   en.wikipedia.org/wiki/Analyse-it

   Analyse-it Method Validation edition provides the standard Analyse-it statistical analyses above, plus procedures for method evaluation, validation and demonstration, including Bland–Altman bias plots, Linear regression, Weighted Linear regression, Deming regression, Weighted Deming regression and Passing Bablok for method comparison ...

6. Line search - Wikipedia

   en.wikipedia.org/wiki/Line_search

   The descent direction can be computed by various methods, such as gradient descent or quasi-Newton method. The step size can be determined either exactly or inexactly. Here is an example gradient method that uses a line search in step 5:

7. Conjugate gradient method - Wikipedia

   en.wikipedia.org/wiki/Conjugate_gradient_method

   The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Arnoldi/Lanczos iteration for eigenvalue problems. Despite differences in their approaches, these derivations share a common topic—proving the orthogonality of the ...