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  2. Tridiagonal matrix algorithm - Wikipedia

    en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm

    In numerical linear algebra, the tridiagonal matrix algorithm, also known as the Thomas algorithm (named after Llewellyn Thomas), is a simplified form of Gaussian elimination that can be used to solve tridiagonal systems of equations. A tridiagonal system for n unknowns may be written as

  3. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    For example, to solve a system of n equations for n unknowns by performing row operations on the matrix until it is in echelon form, and then solving for each unknown in reverse order, requires n(n + 1)/2 divisions, (2n 3 + 3n 2 − 5n)/6 multiplications, and (2n 3 + 3n 2 − 5n)/6 subtractions, [10] for a total of approximately 2n 3 /3 operations.

  4. System of linear equations - Wikipedia

    en.wikipedia.org/wiki/System_of_linear_equations

    The simplest method for solving a system of linear equations is to repeatedly eliminate variables. This method can be described as follows: In the first equation, solve for one of the variables in terms of the others. Substitute this expression into the remaining equations. This yields a system of equations with one fewer equation and unknown.

  5. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    First, we solve the equation = for y. Second, we solve the equation U x = y {\textstyle U\mathbf {x} =\mathbf {y} } for x . In both cases we are dealing with triangular matrices ( L and U ), which can be solved directly by forward and backward substitution without using the Gaussian elimination process (however we do need this process or ...

  6. Elementary algebra - Wikipedia

    en.wikipedia.org/wiki/Elementary_algebra

    Another way of solving the same system of linear equations is by substitution. {+ = = An equivalent for y can be deduced by using one of the two equations. Using the second equation: = Subtracting from each side of the equation:

  7. Successive over-relaxation - Wikipedia

    en.wikipedia.org/wiki/Successive_over-relaxation

    A similar method can be used for any slowly converging iterative process. It was devised simultaneously by David M. Young Jr. and by Stanley P. Frankel in 1950 for the purpose of automatically solving linear systems on digital computers. Over-relaxation methods had been used before the work of Young and Frankel.

  8. Substitution (logic) - Wikipedia

    en.wikipedia.org/wiki/Substitution_(logic)

    The identity substitution, which maps every variable to itself, is the neutral element of substitution composition. A substitution σ is called idempotent if σσ = σ, and hence tσσ = tσ for every term t. When x i ≠t i for all i, the substitution { x 1 ↦ t 1, …, x k ↦ t k} is idempotent if and only if none of the variables x i ...

  9. Change of variables - Wikipedia

    en.wikipedia.org/wiki/Change_of_variables

    are equivalent to Newton's equations for the function =, where T is the kinetic, and V the potential energy. In fact, when the substitution is chosen well (exploiting for example symmetries and constraints of the system) these equations are much easier to solve than Newton's equations in Cartesian coordinates.