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  2. Convergence tests - Wikipedia

    en.wikipedia.org/wiki/Convergence_tests

    While most of the tests deal with the convergence of infinite series, they can also be used to show the convergence or divergence of infinite products. This can be achieved using following theorem: Let { a n } n = 1 ∞ {\displaystyle \left\{a_{n}\right\}_{n=1}^{\infty }} be a sequence of positive numbers.

  3. 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 ...

  4. Bootstrapping (statistics) - Wikipedia

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

    Vinod (2006), [31] presents a method that bootstraps time series data using maximum entropy principles satisfying the Ergodic theorem with mean-preserving and mass-preserving constraints. There is an R package, meboot, [32] that utilizes the method, which has applications in econometrics and computer science.

  5. Category:Convergence tests - Wikipedia

    en.wikipedia.org/wiki/Category:Convergence_tests

    In mathematics, convergence tests are methods to determine if an infinite series converges or diverges. Pages in category "Convergence tests" The following 17 pages are in this category, out of 17 total.

  6. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    The step size is denoted by (sometimes called the learning rate in machine learning) and here ":=" denotes the update of a variable in the algorithm. In many cases, the summand functions have a simple form that enables inexpensive evaluations of the sum-function and the sum gradient.

  7. Aitken's delta-squared process - Wikipedia

    en.wikipedia.org/wiki/Aitken's_delta-squared_process

    In this example, Aitken's method is applied to a sublinearly converging series and accelerates convergence considerably. The convergence is still sublinear, but much faster than the original convergence: the first A [ X ] {\textstyle A[X]} value, whose computation required the first three X {\textstyle X} values, is closer to the limit than the ...

  8. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectation–maximization...

    The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay includes simple examples of the EM algorithm such as clustering using the soft k-means algorithm, and emphasizes the variational view of the EM algorithm, as described in Chapter 33.7 of version 7.2 (fourth edition).

  9. Dirichlet's test - Wikipedia

    en.wikipedia.org/wiki/Dirichlet's_test

    In mathematics, Dirichlet's test is a method of testing for the convergence of a series that is especially useful for proving conditional convergence. It is named after its author Peter Gustav Lejeune Dirichlet , and was published posthumously in the Journal de Mathématiques Pures et Appliquées in 1862.