Linked from

The 25 pages that link to Asymptotic analysis, each with the reason it gives.

• Limit of a functionRelated: Limits compare growth rates and quantify approximation errors at large inputs.

• Computational complexity theoryRelated: It expresses resource growth without tying results to a particular machine.

• Time complexityRelated: It turns running-time measurements into statements about growth at large input sizes.

• Analytic number theoryRelated: Prime-counting results depend on controlling errors in asymptotic estimates.

• Random graphRelated: Many random graph theorems describe properties as the vertex count grows.

• Catalan numberRelated: It gives the growth rate Cₙ ∼ 4ⁿ/(√π n³ᐟ²).

• Bessel functionRelated: Large-argument asymptotics reveal the oscillatory wave behavior of Bessel solutions.

• Mellin transformRelated: Poles of a Mellin transform encode terms in asymptotic expansions.

• Donald KnuthRelated: Asymptotic notation expresses how algorithm costs grow with input size.

• Indeterminate formRelated: Comparing dominant growth rates distinguishes outcomes hidden in ∞/∞ and ∞−∞ forms.

• L'Hôpital's ruleRelated: Derivative ratios can establish which terms dominate in a quotient's limiting behavior.

• The Art of Computer ProgrammingRelated: Asymptotic notation summarizes algorithm growth while preserving precise mathematical comparisons.

• Blum's speedup theoremRelated: Speedup factors describe cost differences for sufficiently large inputs.

• De Moivre–Laplace theoremRelated: The trial count tends to infinity in the theorem's limiting statement.

• Faà di Bruno's formulaRelated: Higher derivatives of composed functions enter expansions and estimates of asymptotic behavior.

• Cameron–Erdős conjectureRelated: The conjecture specifies the order of growth rather than an exact count.