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Euler contains libraries for statistics, exact numerical computations with interval inclusions, differential equations and stiff equations, astronomical functions, geometry, and more. The clean interface consists of a text window, and a graphics window. The text window contains fully editable notebooks, and the graphics window the graphics output.
The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b
The Numerical Recipes books cover a range of topics that include both classical numerical analysis (interpolation, integration, linear algebra, differential equations, and so on), signal processing (Fourier methods, filtering), statistical treatment of data, and a few topics in machine learning (hidden Markov model, support vector machines).
Multiple roots are highly sensitive, known to be ill-conditioned and inaccurate in numerical computation in general. A method by Zhonggang Zeng (2004), implemented as a MATLAB package, computes multiple roots and corresponding multiplicities of a polynomial accurately even if the coefficients are inexact. [3] [4] [5]
The MacCormack method is well suited for nonlinear equations (Inviscid Burgers equation, Euler equations, etc.) The order of differencing can be reversed for the time step (i.e., forward/backward followed by backward/forward). For nonlinear equations, this procedure provides the best results.
If nothing is specified, the equation is rendered in the same display style as "block", but without using a new paragraph. If the equation does appear on a line by itself, it is not automatically indented. The sum = converges to 2. The next line-width is disturbed by large operators. Or: The sum
Method of lines - the example, which shows the origin of the name of method. The method of lines (MOL, NMOL, NUMOL [ 1 ] [ 2 ] [ 3 ] ) is a technique for solving partial differential equations (PDEs) in which all but one dimension is discretized.
As an example, consider the advection equation (this example assumes familiarity with PDE notation, and solutions to basic ODEs). + = where is constant and is a function of and . We want to transform this linear first-order PDE into an ODE along the appropriate curve; i.e. something of the form