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  2. Linear multistep method - Wikipedia

    en.wikipedia.org/wiki/Linear_multistep_method

    Multistep methods attempt to gain efficiency by keeping and using the information from previous steps rather than discarding it. Consequently, multistep methods refer to several previous points and derivative values. In the case of linear multistep methods, a linear combination of the previous points and derivative values is used.

  3. Zero stability - Wikipedia

    en.wikipedia.org/wiki/Zero_stability

    The roots of this equation are = and = and so the general solution to the recurrence relation is = + (). Rounding errors in the computation of y 1 {\displaystyle y_{1}} would mean a nonzero (though small) value of c 2 {\displaystyle c_{2}} so that eventually the parasitic solution ( − 5 ) n {\displaystyle (-5)^{n}} would dominate.

  4. General linear methods - Wikipedia

    en.wikipedia.org/wiki/General_linear_methods

    General linear methods (GLMs) are a large class of numerical methods used to obtain numerical solutions to ordinary differential equations. They include multistage Runge–Kutta methods that use intermediate collocation points , as well as linear multistep methods that save a finite time history of the solution.

  5. Numerical methods for ordinary differential equations

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

    Explicit examples from the linear multistep family include the Adams–Bashforth methods, and any Runge–Kutta method with a lower diagonal Butcher tableau is explicit. A loose rule of thumb dictates that stiff differential equations require the use of implicit schemes, whereas non-stiff problems can be solved more efficiently with explicit ...

  6. Backward differentiation formula - Wikipedia

    en.wikipedia.org/wiki/Backward_differentiation...

    The backward differentiation formula (BDF) is a family of implicit methods for the numerical integration of ordinary differential equations.They are linear multistep methods that, for a given function and time, approximate the derivative of that function using information from already computed time points, thereby increasing the accuracy of the approximation.

  7. Trapezoidal rule (differential equations) - Wikipedia

    en.wikipedia.org/wiki/Trapezoidal_rule...

    In numerical analysis and scientific computing, the trapezoidal rule is a numerical method to solve ordinary differential equations derived from the trapezoidal rule for computing integrals. The trapezoidal rule is an implicit second-order method, which can be considered as both a Runge–Kutta method and a linear multistep method.

  8. List of numerical analysis topics - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical_analysis...

    Linear-fractional programming — objective is ratio of linear functions, constraints are linear Fractional programming — objective is ratio of nonlinear functions, constraints are linear; Nonlinear complementarity problem (NCP) — find x such that x ≥ 0, f(x) ≥ 0 and x T f(x) = 0; Least squares — the objective function is a sum of squares

  9. Exponential integrator - Wikipedia

    en.wikipedia.org/wiki/Exponential_integrator

    In the case of , this formulation is the exact solution to the linear differential equation. Numerical methods require a discretization of equation (2). They can be based on Runge-Kutta discretizations, [5] [6] [7] linear multistep methods or a variety of other options.

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