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  2. Randall J. LeVeque - Wikipedia

    en.wikipedia.org/wiki/Randall_J._LeVeque

    Among other contributions, he is lead developer of the open source software project Clawpack for solving hyperbolic partial differential equations using the finite volume method. With Zhilin Li, he has also devised a numerical technique called the immersed interface method for solving problems with elastic boundaries or surface tension. [2] [3]

  3. Finite difference method - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_method

    To use a finite difference method to approximate the solution to a problem, one must first discretize the problem's domain. This is usually done by dividing the domain into a uniform grid (see image). This means that finite-difference methods produce sets of discrete numerical approximations to the derivative, often in a "time-stepping" manner.

  4. Finite difference methods for option pricing - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_methods...

    Finite difference methods were first applied to option pricing by Eduardo Schwartz in 1977. [2] [3]: 180 In general, finite difference methods are used to price options by approximating the (continuous-time) differential equation that describes how an option price evolves over time by a set of (discrete-time) difference equations.

  5. Finite difference coefficient - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_coefficient

    Backward finite difference [ edit ] To get the coefficients of the backward approximations from those of the forward ones, give all odd derivatives listed in the table in the previous section the opposite sign, whereas for even derivatives the signs stay the same.

  6. Finite difference - Wikipedia

    en.wikipedia.org/wiki/Finite_difference

    In an analogous way, one can obtain finite difference approximations to higher order derivatives and differential operators. For example, by using the above central difference formula for f ′(x + ⁠ h / 2 ⁠) and f ′(x − ⁠ h / 2 ⁠) and applying a central difference formula for the derivative of f ′ at x, we obtain the central difference approximation of the second derivative of f:

  7. Lax–Friedrichs method - Wikipedia

    en.wikipedia.org/wiki/Lax–Friedrichs_method

    The Lax–Friedrichs method, named after Peter Lax and Kurt O. Friedrichs, is a numerical method for the solution of hyperbolic partial differential equations based on finite differences. The method can be described as the FTCS (forward in time, centered in space) scheme with a numerical dissipation term of 1/2.

  8. Stencil (numerical analysis) - Wikipedia

    en.wikipedia.org/wiki/Stencil_(numerical_analysis)

    The finite difference coefficients for a given stencil are fixed by the choice of node points. The coefficients may be calculated by taking the derivative of the Lagrange polynomial interpolating between the node points, [3] by computing the Taylor expansion around each node point and solving a linear system, [4] or by enforcing that the stencil is exact for monomials up to the degree of the ...

  9. FTCS scheme - Wikipedia

    en.wikipedia.org/wiki/FTCS_scheme

    In numerical analysis, the FTCS (forward time-centered space) method is a finite difference method used for numerically solving the heat equation and similar parabolic partial differential equations. [1] It is a first-order method in time, explicit in time, and is conditionally stable when applied to the heat equation.