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  2. Polynomial interpolation - Wikipedia

    en.wikipedia.org/wiki/Polynomial_interpolation

    For example, given a = f(x) = a 0 x 0 + a 1 x 1 + ··· and b = g(x) = b 0 x 0 + b 1 x 1 + ···, the product ab is a specific value of W(x) = f(x)g(x). One may easily find points along W(x) at small values of x, and interpolation based on those points will yield the terms of W(x) and the specific product ab. As fomulated in Karatsuba ...

  3. Legendre polynomials - Wikipedia

    en.wikipedia.org/wiki/Legendre_polynomials

    The Legendre polynomials were first introduced in 1782 by Adrien-Marie Legendre [3] as the coefficients in the expansion of the Newtonian potential | ′ | = + ′ ′ ⁡ = = ′ + (⁡), where r and r′ are the lengths of the vectors x and x′ respectively and γ is the angle between those two vectors.

  4. Orthogonal coordinates - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_coordinates

    A conformal map acting on a rectangular grid. Note that the orthogonality of the curved grid is retained. While vector operations and physical laws are normally easiest to derive in Cartesian coordinates, non-Cartesian orthogonal coordinates are often used instead for the solution of various problems, especially boundary value problems, such as those arising in field theories of quantum ...

  5. Orthogonal polynomials - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_polynomials

    Orthogonal polynomials with matrices have either coefficients that are matrices or the indeterminate is a matrix. There are two popular examples: either the coefficients { a i } {\displaystyle \{a_{i}\}} are matrices or x {\displaystyle x} :

  6. Orthogonality principle - Wikipedia

    en.wikipedia.org/wiki/Orthogonality_principle

    The orthogonality principle is most commonly used in the setting of linear estimation. [1] In this context, let x be an unknown random vector which is to be estimated based on the observation vector y. One wishes to construct a linear estimator ^ = + for some matrix H and vector c.

  7. Orthogonal functions - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_functions

    Plot of the Chebyshev rational functions of order n=0,1,2,3 and 4 between x=0.01 and 100. Legendre and Chebyshev polynomials provide orthogonal families for the interval [−1, 1] while occasionally orthogonal families are required on [0, ∞). In this case it is convenient to apply the Cayley transform first, to bring the argument into [−1, 1].

  8. Lagrange polynomial - Wikipedia

    en.wikipedia.org/wiki/Lagrange_polynomial

    To find () at a point , define a new function () = ~ = () ~ and choose ~ = = where is the constant we are required to determine for a given . We choose C {\displaystyle C} so that F ( x ) {\displaystyle F(x)} has k + 2 {\displaystyle k+2} zeroes (at all nodes and x p {\displaystyle x_{p}} ) between x 0 {\displaystyle x_{0}} and x k ...

  9. Orthogonality (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Orthogonality_(mathematics)

    In mathematics, orthogonality is the generalization of the geometric notion of perpendicularity to the linear algebra of bilinear forms. Two elements u and v of a vector space with bilinear form B {\displaystyle B} are orthogonal when B ( u , v ) = 0 {\displaystyle B(\mathbf {u} ,\mathbf {v} )=0} .