Titlebook: Computational Physics; Simulation of Classi Philipp O. J. Scherer Textbook 20101st edition Springer-Verlag Berlin Heidelberg 2010 Algorithm

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书目名称Computational Physics影响因子(影响力)




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书目名称Computational Physics网络公开度




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书目名称Computational Physics年度引用




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https://doi.org/10.1007/978-3-7908-1954-0 the sampling frequency of audio or video signals, interpolation methods are necessary. But interpolation is also helpful to develop more sophisticated numerical methods for the calculation of numerical derivatives or integrals. Polynomial interpolation is discussed in large detail together with its

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https://doi.org/10.1007/978-3-7908-1954-0on with pivoting are sufficient which decompose the coefficient matrix into a product of a lower and an upper triangular matrix. An alternative method is QR decomposition which is numerically more stable in certain cases. Both these methods are compared in a computer experiment. Special and very eff

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https://doi.org/10.1007/978-3-7908-1954-0points can be found as the roots of the derivative. Elementary methods in one dimension are bisection, the regula falsi method, the Newton–Raphson, and secant methods. The efficiency of these methods is compared in a computer experiment. For functions of more than one variable the method of steepest

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Theory and Theoretical Framework,of thermodynamical averages Monte Carlo methods are very useful which sample the integration volume at randomly chosen points. This chapter begins with some basic statistics. Probability density and cumulative distribution are introduced. The construction of a histogram is described. The central lim

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https://doi.org/10.1007/978-1-4471-4591-2ymmetric, if real) matrix. The direct solution of the eigenvalue problem is only possible for matrices of very small dimension. For medium-sized problems the Jacobi method or reduction to tridiagonal form by a series of Householder reflections are appropriate. Special algorithms are available for ma

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Oksana Soshko,Yuri Merkuryev,Martins Chakstescribe a functional relationship between two or more variables by a smooth curve, i.e., to fit a certain model to the data. If uncertainties of the data are negligibly small, an exact fit is possible, for instance, with polynomials, spline functions, or trigonometric functions. If the uncertainties