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  2. Relaxation (iterative method) - Wikipedia

    en.wikipedia.org/wiki/Relaxation_(iterative_method)

    Relaxation methods were developed for solving large sparse linear systems, which arose as finite-difference discretizations of differential equations. [ 2 ] [ 3 ] They are also used for the solution of linear equations for linear least-squares problems [ 4 ] and also for systems of linear inequalities, such as those arising in linear programming .

  3. Newton–Krylov method - Wikipedia

    en.wikipedia.org/wiki/Newton–Krylov_method

    Newton–Krylov methods are numerical methods for solving non-linear problems using Krylov subspace linear solvers. [1] [2] Generalising the Newton method to systems of multiple variables, the iteration formula includes a Jacobian matrix. Solving this directly would involve calculation of the Jacobian's inverse, when the Jacobian matrix itself ...

  4. Nonlinear system - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_system

    In mathematics and science, a nonlinear system (or a non-linear system) is a system in which the change of the output is not proportional to the change of the input. [1] [2] Nonlinear problems are of interest to engineers, biologists, [3] [4] [5] physicists, [6] [7] mathematicians, and many other scientists since most systems are inherently nonlinear in nature. [8]

  5. Iterative method - Wikipedia

    en.wikipedia.org/wiki/Iterative_method

    In the absence of rounding errors, direct methods would deliver an exact solution (for example, solving a linear system of equations = by Gaussian elimination). Iterative methods are often the only choice for nonlinear equations. However, iterative methods are often useful even for linear problems involving many variables (sometimes on the ...

  6. Numerical continuation - Wikipedia

    en.wikipedia.org/wiki/Numerical_continuation

    Numerical continuation is a method of computing approximate solutions of a system of parameterized nonlinear equations, (,) = [1]The parameter is usually a real scalar and the solution is an n-vector.

  7. Gauss–Newton algorithm - Wikipedia

    en.wikipedia.org/wiki/Gauss–Newton_algorithm

    Note that quasi-Newton methods can minimize general real-valued functions, whereas Gauss–Newton, Levenberg–Marquardt, etc. fits only to nonlinear least-squares problems. Another method for solving minimization problems using only first derivatives is gradient descent. However, this method does not take into account the second derivatives ...

  8. Homotopy analysis method - Wikipedia

    en.wikipedia.org/wiki/Homotopy_analysis_method

    Further, the HAM uses the homotopy parameter only on a theoretical level to demonstrate that a nonlinear system may be split into an infinite set of linear systems which are solved analytically, while the continuation methods require solving a discrete linear system as the homotopy parameter is varied to solve the nonlinear system.

  9. Numerical methods for ordinary differential equations - Wikipedia

    en.wikipedia.org/wiki/Numerical_methods_for...

    The step size is =. The same illustration for = The midpoint method converges faster than the Euler method, as .. Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs).