Asymptotic analysis

Asymptotic analysis describes what remains dominant as an argument approaches a specified limit. It compares functions or sequences by scale: how much of a complicated expression can be replaced by a simpler one, and how large is the omitted part? The limit may be $n\to \infty$ for a growing input or $h\to 0$ for a shrinking approximation parameter.

For example, $n^2+10^{5}n$ is eventually dominated by its quadratic term, since division by $n^2$ gives $1+10^5/n\to 1$. But at $n=100$, the linear term contributes $10^7$, compared with only $10^4$ from the quadratic term. A statement about the limiting scale and a good approximation at a particular input are different claims.

Three useful comparisons keep their strengths distinct. Big O notation supplies an eventual upper bound up to a constant multiplier; big theta notation supplies both an upper bound and a lower bound up to constants; asymptotic equivalence makes the ratio tend to one. Little o notation says that a term becomes negligible relative to a specified scale. These distinctions make approximations checkable instead of merely calling one expression small.

The same language connects time complexity with Taylor remainders. In an operation-count model, one asks how a cost scales with input size. In a Taylor expansion, one asks how the approximation error scales with the increment. Neither use requires interpreting the unspecified constant as one. The asymptotic comparison of exponential functions gives another example: dividing by the largest contributing exponential reveals the terms that disappear.