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  2. Omega constant - Wikipedia

    en.wikipedia.org/wiki/Omega_constant

    The omega constant is a mathematical constant defined as the unique real number that satisfies the equation = It is the value of W(1), where W is Lambert's W function. The name is derived from the alternate name for Lambert's W function, the omega function. The numerical value of Ω is given by

  3. Successive over-relaxation - Wikipedia

    en.wikipedia.org/wiki/Successive_over-relaxation

    Spectral radius () of the iteration matrix for the SOR method .The plot shows the dependence on the spectral radius of the Jacobi iteration matrix := ().. The choice of relaxation factor ω is not necessarily easy, and depends upon the properties of the coefficient matrix.

  4. Lambert W function - Wikipedia

    en.wikipedia.org/wiki/Lambert_W_function

    The product logarithm Lambert W function plotted in the complex plane from −2 − 2i to 2 + 2i The graph of y = W(x) for real x < 6 and y > −4.The upper branch (blue) with y ≥ −1 is the graph of the function W 0 (principal branch), the lower branch (magenta) with y ≤ −1 is the graph of the function W −1.

  5. Chaitin's constant - Wikipedia

    en.wikipedia.org/wiki/Chaitin's_constant

    In the computer science subfield of algorithmic information theory, a Chaitin constant (Chaitin omega number) [1] or halting probability is a real number that, informally speaking, represents the probability that a randomly constructed program will halt.

  6. List of mathematical constants - Wikipedia

    en.wikipedia.org/wiki/List_of_mathematical_constants

    A mathematical constant is a key number whose value is fixed by an unambiguous definition, often referred to by a symbol (e.g., an alphabet letter), or by mathematicians' names to facilitate using it across multiple mathematical problems. [1]

  7. Jacobi method - Wikipedia

    en.wikipedia.org/wiki/Jacobi_method

    In numerical linear algebra, the Jacobi method (a.k.a. the Jacobi iteration method) is an iterative algorithm for determining the solutions of a strictly diagonally dominant system of linear equations. Each diagonal element is solved for, and an approximate value is plugged in. The process is then iterated until it converges.

  8. Grover's algorithm - Wikipedia

    en.wikipedia.org/wiki/Grover's_algorithm

    Grover's algorithm is optimal up to sub-constant factors. That is, any algorithm that accesses the database only by using the operator U ω must apply U ω at least a () fraction as many times as Grover's algorithm. [21] The extension of Grover's algorithm to k matching entries, π (N/k) 1/2 /4, is also optimal. [18]

  9. Modified Richardson iteration - Wikipedia

    en.wikipedia.org/wiki/Modified_Richardson_iteration

    Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method. We seek the solution to a set of linear equations, expressed in matrix terms as