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Alternatively, when the inner product of the function being approximated cannot be evaluated, the discrete orthogonality condition gives an often useful result for approximate coefficients: = (+), where δ ij is the Kronecker delta function and the x k are the N Gauss–Chebyshev zeros of T N (x): = ((+)).
The following illustration shows the Chebyshev filters next to other common filter types obtained with the same number of coefficients (fifth order): Chebyshev filters are sharper than the Butterworth filter; they are not as sharp as the elliptic one, but they show fewer ripples over the bandwidth.
The Chebyshev functions, especially the second one ψ (x), are often used in proofs related to prime numbers, because it is typically simpler to work with them than with the prime-counting function, π (x) (see the exact formula below.) Both Chebyshev functions are asymptotic to x, a statement equivalent to the prime number theorem.
Chebyshev's equation is the second order linear differential equation ... where the coefficients obey the recurrence relation + = ...
The ephemerides are now available via World Wide Web and FTP [13] as data files containing the Chebyshev coefficients, along with source code to recover (calculate) positions and velocities. [14] Files vary in the time periods they cover, ranging from a few hundred years to several thousand, and bodies they include.
In applied mathematics, a discrete Chebyshev transform (DCT) is an analog of the discrete Fourier transform for a function of a real interval, converting in either direction between function values at a set of Chebyshev nodes and coefficients of a function in Chebyshev polynomial basis. Like the Chebyshev polynomials, it is named after Pafnuty ...
Many applications for Chebyshev nodes, such as the design of equally terminated passive Chebyshev filters, cannot use Chebyshev nodes directly, due to the lack of a root at 0. However, the Chebyshev nodes may be modified into a usable form by translating the roots down such that the lowest roots are moved to zero, thereby creating two roots at ...
Chebyshev's inequality is important because of its applicability to any distribution. As a result of its generality it may not (and usually does not) provide as sharp a bound as alternative methods that can be used if the distribution of the random variable is known.