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2. Linear multistep method - Wikipedia

   en.wikipedia.org/wiki/Linear_multistep_method

   Numerical methods for ordinary differential equations approximate solutions to initial value problems of the form ′ = (,), =.. The result is approximations for the value of () at discrete times : = +, where is the time step (sometimes referred to as ) and is an integer.

3. Numerical methods for ordinary differential equations

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

   For example, the second-order equation y′′ = −y can be rewritten as two first-order equations: y′ = z and z′ = −y. In this section, we describe numerical methods for IVPs, and remark that boundary value problems (BVPs) require a different set of tools. In a BVP, one defines values, or components of the solution y at more than one ...

4. Lax–Wendroff method - Wikipedia

   en.wikipedia.org/wiki/Lax–Wendroff_method

   What follows is the Richtmyer two-step Lax–Wendroff method. The first step in the Richtmyer two-step Lax–Wendroff method calculates values for f(u(x, t)) at half time steps, t n + 1/2 and half grid points, x i + 1/2. In the second step values at t n + 1 are calculated using the data for t n and t n + 1/2.

5. Newton's method - Wikipedia

   en.wikipedia.org/wiki/Newton's_method

   An illustration of Newton's method. In numerical analysis, the Newton–Raphson method, also known simply as Newton's method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.

6. Reduction of order - Wikipedia

   en.wikipedia.org/wiki/Reduction_of_order

   Consider the general, homogeneous, second-order linear constant coefficient ordinary differential equation. (ODE) ″ + ′ + =, where ,, are real non-zero coefficients. . Two linearly independent solutions for this ODE can be straightforwardly found using characteristic equations except for the case when the discriminant, , vanish

7. Newton's method in optimization - Wikipedia

   en.wikipedia.org/wiki/Newton's_method_in...

   The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.