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The function e (−1/x 2) is not analytic at x = 0: the Taylor series is identically 0, although the function is not. If f ( x ) is given by a convergent power series in an open disk centred at b in the complex plane (or an interval in the real line), it is said to be analytic in this region.
The filter was popularized in the field of economics in the 1990s by economists Robert J. Hodrick and Nobel Memorial Prize winner Edward C. Prescott, [1] though it was first proposed much earlier by E. T. Whittaker in 1923. [2], see Whittaker-Henderson smoothing. The Hodrick-Prescott filter is a special case of a smoothing spline. [3]
For any real numbers (scalars) x and y we know that the exponential function satisfies e x+y = e x e y. The same is true for commuting matrices. If matrices X and Y commute (meaning that XY = YX), then, + =. However, for matrices that do not commute the above equality does not necessarily hold.
In this setting, e 0 = 1, and e x is invertible with inverse e −x for any x in B. If xy = yx, then e x + y = e x e y, but this identity can fail for noncommuting x and y. Some alternative definitions lead to the same function. For instance, e x can be defined as (+).
Here φ is the angle that a line connecting the origin with a point on the unit circle makes with the positive real axis, measured counterclockwise and in radians. The original proof is based on the Taylor series expansions of the exponential function e z (where z is a complex number) and of sin x and cos x for real numbers x .
The solution with the function value can be found after 325 function evaluations. Using the Nelder–Mead method from starting point x 0 = ( − 1 , 1 ) {\displaystyle x_{0}=(-1,1)} with a regular initial simplex a minimum is found with function value 1.36 ⋅ 10 − 10 {\displaystyle 1.36\cdot 10^{-10}} after 185 function evaluations.
In probability theory, it is possible to approximate the moments of a function f of a random variable X using Taylor expansions, provided that f is sufficiently differentiable and that the moments of X are finite. A simulation-based alternative to this approximation is the application of Monte Carlo simulations.
Modular exponentiation is the remainder when an integer b (the base) is raised to the power e (the exponent), and divided by a positive integer m (the modulus); that is, c = b e mod m. From the definition of division, it follows that 0 ≤ c < m .