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Computing the real roots of a Fourier series-plus-linear-polynomial: A Chebyshev companion matrix approach

机译:计算傅里叶级数加线性多项式的实根:一种切比雪夫伴随矩阵方法

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Fourier series often need to be generalized by appending a linear polynomial to the usual series of sines and cosines. The integral of a trigonometric polynomial is one example; another is a time series of climate data where the periodic oscillations of the diurnal and annual cycles are accompanied by a non-periodic trend (global warming). Stock market averages fluctuate about a generally upward trend. Such non-periodic variations with time are commonly called "secular trends". We borrow "secular" to label a truncated Fourier series plus a linear trend as a "linear", secular trigonometric polynomial. Standard Fourier rootfinding methods are wrecked by the extra, nonperiodic term. Therefore, we introduce a new algorithm for computing the zeros of a Fourier polynomial-with-secular-trend. First, we expand the linear secular trigonometric polynomial f _N(t) as a truncated Chebyshev series. Because of the special structure, it is easy to calculate a problem-dependent truncation M such that the error of the truncated Chebyshev series is guaranteed to be less than a user-specified tolerance. We then find the roots of the truncated Chebyshev series as the eigenvalues of the Chebyshev companion matrix. This computes all roots, but we explain why the method is not reliable for complex-valued roots unless these are close to the real axis. No a priori information is required of the user except the coefficients of the linear secular trigonometric polynomial. Numerical examples show that 13 decimal place accuracy for real roots is typical.
机译:傅立叶级数通常需要通过将线性多项式附加到通常的正弦和余弦序列上来进行概括。三角多项式的积分就是一个例子。另一个是气候数据的时间序列,其中日周期和年周期的周期性振荡伴随着非周期性趋势(全球变暖)。股市平均价格在总体上呈上升趋势波动。这种随时间的非周期性变化通常称为“长期趋势”。我们借用“世俗的”来标记截短的傅立叶级数加上线性趋势,作为“线性的”世俗的三角多项式。标准的傅立叶寻根方法使额外的非周期性术语破坏了。因此,我们引入了一种新的算法,用于计算带趋势的傅立叶多项式的零。首先,我们将线性世俗三角多项式f _N(t)扩展为截断的Chebyshev级数。由于特殊的结构,可以很容易地计算出与问题相关的截短M,以确保截断的切比雪夫序列的误差小于用户指定的公差。然后,我们将截断的切比雪夫序列的根作为切比雪夫伴随矩阵的特征值。这将计算所有根,但是我们解释了为什么除非复数根接近实轴,否则该方法对于复值根不可靠。除了线性世俗三角多项式的系数之外,用户不需要先验信息。数值示例表明,实数根的小数位数精度通常为13。

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