Approximations and expansions Concept
Big O notation
Summary
Big O notation compares the growth of two functions. The statement means that is at most a constant times near a given point or at infinity. The node big O notation gives the precise definition.
Where this leads
29 pages build on this one.
- Fundamental strip of a Mellin transform
- Cap set problem
- Low-discrepancy sequence
- Gauss circle problem
- Saturation of approximation methods
- Mertens' theorems
- Rader's FFT algorithm
- Cooley–Tukey FFT algorithm
- Bombieri–Vinogradov theorem
- Prime-factor FFT algorithm
- Vinogradov's theorem
- Differentiability of the Mellin transform
- Numerical methods for ordinary differential equations
- Strassen algorithm
- Lindelöf hypothesis
- Numerical stability
- Hardy–Littlewood circle method
- Bluestein's FFT algorithm
- Fast Fourier transform
- Pseudo-spectral method
- Asymptotic analysis
- Numerical differentiation
- Akra–Bazzi theorem
- Chen's theorem
- Matrix multiplication exponent
- Siegel–Walfisz theorem
- Bounded gaps between primes
- Local truncation error
- Asymptotic expansion from poles of a Mellin transform
Further reading
- Web https://en.wikipedia.org/wiki/Big_O_notation, lead section
About this page
| Tag | 0TGV80E6A4 |
|---|---|
| URI | m2://0TGV80E6A4 |
| Kind | concept |
| Writing | W1 stated |
| MSC | 41A60 |
| Same as |
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What this page needs for W2
- edge instance_of in: at least two examples (0 of 2)
- edge not_instance_of in: at least one non-example (0 of 1)
- edge specializes both: the more general notions and the special cases (0 of 1)
The writing levels say how complete the text of a page is. They do not measure its truth.
What links here
Definitions
Used by
- Fundamental strip of a Mellin transform
- Cap set problem
- Low-discrepancy sequence
- Gauss circle problem
- Saturation of approximation methods
- Mertens' theorems
- Cooley–Tukey FFT algorithm
- Differentiability of the Mellin transform
- Numerical methods for ordinary differential equations
- Strassen algorithm
- Lindelöf hypothesis
- Numerical stability
- Bluestein's FFT algorithm
- Fast Fourier transform
- Asymptotic analysis
- Numerical differentiation
- Akra–Bazzi theorem
- Matrix multiplication exponent
- Siegel–Walfisz theorem
- Local truncation error