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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. Midpoint method - Wikipedia

    en.wikipedia.org/wiki/Midpoint_method

    In numerical analysis, a branch of applied mathematics, the midpoint method is a one-step method for numerically solving the differential equation, ′ = (, ()), =. The explicit midpoint method is given by the formula

  4. One-step method - Wikipedia

    en.wikipedia.org/wiki/One-step_method

    The decisive step in the direction of differential equation models is now the reverse question: In the example of the moving body, let the velocity () be known at every point in time 𝑡 and its position () be determined from this. It is clear that the initial position of the body at a point in time 𝑡 0 must also be known in order to be ...

  5. Linear multistep method - Wikipedia

    en.wikipedia.org/wiki/Linear_multistep_method

    Linear multistep methods are used for the numerical solution of ordinary differential equations. Conceptually, a numerical method starts from an initial point and then takes a short step forward in time to find the next solution point. The process continues with subsequent steps to map out the solution.

  6. Euler method - Wikipedia

    en.wikipedia.org/wiki/Euler_method

    The exact solution of the differential equation is () =, so () =. Although the approximation of the Euler method was not very precise in this specific case, particularly due to a large value step size h {\displaystyle h} , its behaviour is qualitatively correct as the figure shows.

  7. Explicit and implicit methods - Wikipedia

    en.wikipedia.org/wiki/Explicit_and_implicit_methods

    Explicit methods calculate the state of a system at a later time from the state of the system at the current time, while implicit methods find a solution by solving an equation involving both the current state of the system and the later one.