# Function actions with large variables

Whenever I think of just how a function acts, I constantly attempt to recognize a basic pattern of actions with some usual numbers (someplace in between 5 and also 100 possibly) and afterwards I attempt to see if anything intriguing takes place around 1, 0 and also right into adverse numbers if relevant.

If that all exercises, I basically think that I recognize that the function is mosting likely to act in a similar way for large numbers as it provides for those reasonably handful.

Exist remarkable (renowned, brilliant or usual) functions where large numbers would certainly create them to act dramatically in different ways than would originally be assumed if I followed my normal speculative pattern? If so, exist any kind of indication I should recognize?

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2019-05-07 09:45:44
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Answers: 4

Many sensible functions $f(x)=\frac{n(x)}{d(x)}=q(x)+\frac{r(x)}{d(x)}$ (where deg (r) < deg (d)) act really in different ways in the basic location of the absolutely nos of d (x) than for huge (favorable or adverse) values x. Near the absolutely nos of d (x), the values of $\frac{r(x)}{d(x)}$ control the values of q (x) (that is, f (x) acts like $\frac{r(x)}{d(x)}$), whereas for huge (favorable or adverse) values of x, the values of q (x) control the values of $\frac{r(x)}{d(x)}$ (that is, f (x) acts like q (x) ).

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2019-05-09 06:12:27
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The Chebyshev bias actions is effectively recognized just by checking out approximately really large numbers.

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2019-05-09 06:11:09
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The values of a function $f(x)$ for reasonably tiny values of its argument $x$ is commonly a really negative forecaster of asymptotic actions of $f(x)$ for huge $x$. This holds true also when $f(x)$ is an analytic function which is distinctly established by its values on any kind of tiny interval $x\in[-\epsilon,\epsilon]$.

Look at this passage from "Concrete Mathematics" for a (not the most awful feasible) instance of just how deceitful the "tiny argument values" instinct can be.

It aids to grow a large perspective when we're doing asymptotic evaluation : We need to assume large, when visualizing a variable that comes close to infinity. As an example, the power structure claims that $\log n\prec n^{0.0001}$ ; this could appear incorrect if we restrict our perspectives to teensy - little numbers like one googol, $n = 10^{100}$. For because instance, $\log n = 100$, while $n^{0.0001}$ is just $10^{0.01}\approx 1.0233$. Yet if we rise to a googolplex, $n = 10^{10^{100}}$, after that $\log n = 10^{100}$ fades in contrast with $n^{0.0001} = 10^{10^{96}}$.

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2019-05-09 06:10:16
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$$f(\mathbf x) = \frac1{4000}\sum_{i=1}^n x_i^2 - \prod_{i=1}^n \cos\left(\frac{x_i}{\sqrt i}\right) + 1$$

which is just one of the unbiased functions made use of in screening optimization formulas, looks entirely various in huge range (controlled by x 2 ) and also tiny range (controlled by cos x ).

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2019-05-09 01:48:23
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