disproportionatecan

Disproportionatecan is a neologism used in theoretical discussions to describe phenomena in which two related measures grow at markedly different rates as input size increases. The term does not belong to any formal discipline with a single agreed definition, but it is used to signal situations where expected correlations between input scale and outcome fail to hold.

Because there is no universal formal definition, readers will encounter varying formulations in different sources. Common informal criteria include: (a) a pair of functions (A(n), B(n)) where A(n) grows without bound relative to B(n) on infinitely many n; (b) a transformation whose output length is disproportionately large compared with the input length for a class of inputs; or (c) a problem instance whose resource requirements inflate more quickly than conventional bounds would suggest.

Examples and analogies are often used to illustrate the idea. For instance, a data-processing pipeline that emits lengthy certificates of length exp(n) for inputs of length n, while the input encoding remains linear, might be described as disproportionatecan in informal terms. In discussions of complexity, the term serves to indicate worst-case inflation where growth cannot be captured by simple polynomial bounds.

Origin and reception: The term appears in informal glossaries, speculative essays, and some online discussions. It is not widely adopted in formal literature, and definitions vary between authors. Readers should rely on the specific definition stated in any given source.

See also: growth rate, scaling, computational complexity.