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  2. 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).

  3. Euler–Maruyama method - Wikipedia

    en.wikipedia.org/wiki/Euler–Maruyama_method

    In Itô calculus, the Euler–Maruyama method (also simply called the Euler method) is a method for the approximate numerical solution of a stochastic differential equation (SDE). It is an extension of the Euler method for ordinary differential equations to stochastic differential equations named after Leonhard Euler and Gisiro Maruyama. The ...

  4. Midpoint method - Wikipedia

    en.wikipedia.org/wiki/Midpoint_method

    The midpoint method computes + so that the red chord is approximately parallel to the tangent line at the midpoint (the green line). In numerical analysis, a branch of applied mathematics, the midpoint method is a one-step method for numerically solving the differential equation,

  5. Linear multistep method - Wikipedia

    en.wikipedia.org/wiki/Linear_multistep_method

    The numerical solution of a one-step method depends on the initial condition , but the numerical solution of an s-step method depend on all the s starting values, ,, …,. It is thus of interest whether the numerical solution is stable with respect to perturbations in the starting values.

  6. Euler method - Wikipedia

    en.wikipedia.org/wiki/Euler_method

    In mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.

  7. Explicit and implicit methods - Wikipedia

    en.wikipedia.org/wiki/Explicit_and_implicit_methods

    In the vast majority of cases, the equation to be solved when using an implicit scheme is much more complicated than a quadratic equation, and no analytical solution exists. Then one uses root-finding algorithms, such as Newton's method, to find the numerical solution. Crank-Nicolson method. With the Crank-Nicolson method

  8. Finite difference method - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_method

    The scheme is always numerically stable and convergent but usually more numerically intensive than the explicit method as it requires solving a system of numerical equations on each time step. The errors are linear over the time step and quadratic over the space step: Δ u = O ( k ) + O ( h 2 ) . {\displaystyle \Delta u=O(k)+O(h^{2}).}

  9. List of numerical-analysis software - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical-analysis...

    APMonitor: APMonitor is a mathematical modeling language for describing and solving representations of physical systems in the form of differential and algebraic equations. Armadillo is C++ template library for linear algebra; includes various decompositions, factorisations, and statistics functions; its syntax ( API ) is similar to MATLAB.

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