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  2. Finite difference method - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_method

    For example, consider the ordinary differential equation ′ = + The Euler method for solving this equation uses the finite difference quotient (+) ′ to approximate the differential equation by first substituting it for u'(x) then applying a little algebra (multiplying both sides by h, and then adding u(x) to both sides) to get (+) + (() +).

  3. 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:

  4. APEX system - Wikipedia

    en.wikipedia.org/wiki/APEX_system

    The implied value (1/3.125) for the speed scaling constant given in the Exif 2.2 specification ("Exif 2.2"; JEITA 2002) differs slightly from the APEX value of / (0.2973); with the Exif value, an ISO arithmetic film speed of 100 corresponds exactly to a speed value [3] of 5.

  5. Finite difference coefficient - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_coefficient

    For arbitrary stencil points and any derivative of order < up to one less than the number of stencil points, the finite difference coefficients can be obtained by solving the linear equations [6] ( s 1 0 ⋯ s N 0 ⋮ ⋱ ⋮ s 1 N − 1 ⋯ s N N − 1 ) ( a 1 ⋮ a N ) = d !

  6. Numerical differentiation - Wikipedia

    en.wikipedia.org/wiki/Numerical_differentiation

    Therefore, the true derivative of f at x is the limit of the value of the difference quotient as the secant lines get closer and closer to being a tangent line: ′ = (+) (). Since immediately substituting 0 for h results in 0 0 {\displaystyle {\frac {0}{0}}} indeterminate form , calculating the derivative directly can be unintuitive.

  7. 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.

  8. Apex graph - Wikipedia

    en.wikipedia.org/wiki/Apex_graph

    A graph is contraction-apex if there is some edge in the graph that can be contracted to make the graph planar. In general, if X is a class of graphs, an "apex-X" graph is a graph that can be brought into the class X by deleting some one vertex. For example, an apex-cograph is a graph G that has a vertex v such that G―v is a cograph.

  9. Difference engine - Wikipedia

    en.wikipedia.org/wiki/Difference_engine

    The principle of a difference engine is Newton's method of divided differences. If the initial value of a polynomial (and of its finite differences) is calculated by some means for some value of X, the difference engine can calculate any number of nearby values, using the method generally known as the method of finite differences.