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  2. Linearization - Wikipedia

    en.wikipedia.org/wiki/Linearization

    This linearization of the system with respect to each of the fields results in a linearized monolithic equation system that can be solved using monolithic iterative solution procedures such as the Newton–Raphson method. Examples of this include MRI scanner systems which results in a system of electromagnetic, mechanical and acoustic fields. [5]

  3. Jacobian matrix and determinant - Wikipedia

    en.wikipedia.org/wiki/Jacobian_matrix_and...

    In vector calculus, the Jacobian matrix (/ dʒ ə ˈ k oʊ b i ə n /, [1] [2] [3] / dʒ ɪ-, j ɪ-/) of a vector-valued function of several variables is the matrix of all its first-order partial derivatives.

  4. Local linearization method - Wikipedia

    en.wikipedia.org/wiki/Local_linearization_method

    In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as the solution of the resulting piecewise linear ...

  5. Linear approximation - Wikipedia

    en.wikipedia.org/wiki/Linear_approximation

    Gaussian optics is a technique in geometrical optics that describes the behaviour of light rays in optical systems by using the paraxial approximation, in which only rays which make small angles with the optical axis of the system are considered. [2] In this approximation, trigonometric functions can be expressed as linear functions of the angles.

  6. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.

  7. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    A pictorial representation of a simple linear program with two variables and six inequalities. The set of feasible solutions is depicted in yellow and forms a polygon, a 2-dimensional polytope. The optimum of the linear cost function is where the red line intersects the polygon.

  8. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    This is the case, for example, if f(x) = x 3 − 2x + 2. For this function, it is even the case that Newton's iteration as initialized sufficiently close to 0 or 1 will asymptotically oscillate between these values. For example, Newton's method as initialized at 0.99 yields iterates 0.99, −0.06317, 1.00628, 0.03651, 1.00196, 0.01162, 1.00020 ...

  9. Hubbert linearization - Wikipedia

    en.wikipedia.org/wiki/Hubbert_linearization

    The Hubbert curve [2] is the first derivative of a logistic function, which has been used for modeling the depletion of crude oil in particular, the depletion of finite mineral resources in general [3] and also population growth patterns. [4] Example of a Hubbert Linearization on the US Lower-48 crude oil production.