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  2. Dormand–Prince method - Wikipedia

    en.wikipedia.org/wiki/Dormand–Prince_method

    Dormand–Prince is the default method in the ode45 solver for MATLAB [4] and GNU Octave [5] and is the default choice for the Simulink's model explorer solver. It is an option in Python's SciPy ODE integration library [6] and in Julia's ODE solvers library. [7] Implementations for the languages Fortran, [8] Java, [9] and C++ [10] are also ...

  3. Finite difference methods for option pricing - Wikipedia

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

    The discrete difference equations may then be solved iteratively to calculate a price for the option. [4] The approach arises since the evolution of the option value can be modelled via a partial differential equation (PDE), as a function of (at least) time and price of underlying; see for example the Black–Scholes PDE. Once in this form, a ...

  4. 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 (+) + (() +).

  5. Topological data analysis - Wikipedia

    en.wikipedia.org/wiki/Topological_data_analysis

    A comparison between these tools is done by Otter et al. [24] Giotto-tda is a Python package dedicated to integrating TDA in the machine learning workflow by means of a scikit-learn API. An R package TDA is capable of calculating recently invented concepts like landscape and the kernel distance estimator. [25]

  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. Pseudo-spectral method - Wikipedia

    en.wikipedia.org/wiki/Pseudo-spectral_method

    In many practical partial differential equations, one has a term that involves derivatives (such as a kinetic energy contribution), and a multiplication with a function (for example, a potential). In the spectral method, the solution ψ {\displaystyle \psi } is expanded in a suitable set of basis functions, for example plane waves,

  8. Xcas - Wikipedia

    en.wikipedia.org/wiki/Xcas

    calculate differential (or derivative) of functions (Figure 2); calculate antiderivative of functions (Figure 2); calculate area and integral calculus; linear algebra [16] Example Xcas commands: produce mixed fractions: propfrac(42/15) gives 2 + ⁠ 4 / 5 ⁠ calculate square root: sqrt(4) = 2

  9. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables ⁡ (+) = ⁡ + ⁡ + ⁡ (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...