Computational methods for physicists : compendium for by Simon Sirca, Martin Horvat

By Simon Sirca, Martin Horvat

This booklet is helping complicated undergraduate, graduate and postdoctoral scholars of their day-by-day paintings via supplying them a compendium of numerical tools. the alternative of tools can pay major realization to errors estimates, balance and convergence concerns in addition to to the how you can optimize application execution speeds. Many examples are given in the course of the chapters, and every bankruptcy is by means of no less than a handful of extra complete difficulties that may be handled, for instance, on a weekly foundation in a one- or two-semester path. In those end-of-chapter difficulties the physics historical past is said, and the most textual content previous them is meant as an advent or as a later reference. much less rigidity is given to the reason of person algorithms. it really is attempted to urge within the reader an personal self sustaining pondering and a specific amount of scepticism and scrutiny rather than blindly following on hand advertisement instruments. learn more... fundamentals of numerical research -- resolution of nonlinear equations -- Matrix equipment -- alterations of services and signs -- Statistical description and modeling of knowledge -- Modeling and research of time sequence -- Initial-value difficulties for usual differential equations -- Boundary-value difficulties for traditional differential equations -- distinction tools for one-dimensional partial differential equations -- distinction tools for partial differential equations in additional than one dim -- Spectral equipment for partial differential equations

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Similar results can be obtained by using other families of orthogonal polynomials; the paper [55] describes further algorithms in which the coefficients of the partial sums cannot be generated as easily, but yield even faster convergence. 93n . However, they require O(n) of memory and O(n2 ) of CPU. 5 Levin’s Transformations Levin’s transformations [56] are among the most generally useful, handy, and efficient methods to accelerate the convergence of series by semi-linear extrapolation. 4 Summation of Finite and Infinite Series 43 We implement them by using divided differences which are computed recursively: δ k fn = δ k−1 fn+1 − δ k−1 fn , tn+k − tn δ 0 fn = f n , where tn = (n + n0 )−1 and we usually take n0 = 0 or n0 = 1.

10 1 Basics of Numerical Analysis then [L/M]f (z) = PL (z) a 0 + a 1 z + a 2 z 2 + · · · + aL z L , = QM (z) b0 + b1 z + b2 z2 + · · · + bM zM b0 = 1, defines a Padé approximation of order (L, M) of the function f . The coefficients ak and bk can be determined by equating the approximation [L/M]f with the power series for f and reading off the coefficients of the same powers of x: b0 + b1 z + · · · + bM zM (c0 + c1 z + · · · ) = a0 + a1 z + · · · + aL zL + O zL+M+1 . By comparing the terms with powers zL+1 , zL+2 , .

3 Power and Asymptotic Expansion, Asymptotic Analysis 29 bn = cn for 0 ≤ n ≤ k − 1. 50). We have assumed that h(x, λ) as a function of λ has a pole of even degree at infinity, h(x, λ) = O(λ2k ). 47) are not valid. In this case we can introduce a new asymptotic parameter λ = λ1/2 and use λ in the formulas derived above. The procedure described here is called the WKB (Wentzel–Kramers–Brillouin) method. The special case k = 1 coincides with the problems of the Schrödinger equation for a particle of mass m in an one-dimensional potential V , − 2 ∂2 ψ(x) + V (x) − E ψ(x) = 0.

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