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  2. Filters in topology - Wikipedia

    en.wikipedia.org/wiki/Filters_in_topology

    Generalizing sequence convergence by using sets − determining sequence convergence without the sequence See also: Limit of a sequence and Net (mathematics) A sequence in X {\displaystyle X} is by definition a map N → X {\displaystyle \mathbb {N} \to X} from the natural numbers into the space X . {\displaystyle X.}

  3. Myopia - Wikipedia

    en.wikipedia.org/wiki/Myopia

    Myopia. Myopia, also known as near-sightedness and short-sightedness, [5] is an eye disease [6][7][8] where light from distant objects focuses in front of, instead of on, the retina. [1][2][7] As a result, distant objects appear blurry while close objects appear normal. [1]

  4. Macular degeneration - Wikipedia

    en.wikipedia.org/wiki/Macular_degeneration

    8.7% global prevalence in 2020 [2] Macular degeneration, also known as age-related macular degeneration (AMD or ARMD), is a medical condition which may result in blurred or no vision in the center of the visual field. [1] Early on there are often no symptoms. [1] Over time, however, some people experience a gradual worsening of vision that may ...

  5. Isaac Newton - Wikipedia

    en.wikipedia.org/wiki/Isaac_Newton

    Isaac Barrow. Succeeded by. William Whiston. Signature. Sir Isaac Newton FRS (25 December 1642 – 20 March 1726/27 [a]) was an English polymath active as a mathematician, physicist, astronomer, alchemist, theologian, and author who was described in his time as a natural philosopher. [7] He was a key figure in the Scientific Revolution and the ...

  6. Laplace transform - Wikipedia

    en.wikipedia.org/wiki/Laplace_transform

    Similarly, the set of values for which F(s) converges (conditionally or absolutely) is known as the region of conditional convergence, or simply the region of convergence (ROC). If the Laplace transform converges (conditionally) at s = s 0 , then it automatically converges for all s with Re( s ) > Re( s 0 ) .

  7. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    A comparison of the convergence of gradient descent with optimal step size (in green) and conjugate vector (in red) for minimizing a quadratic function associated with a given linear system. Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2).

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