Constant-factor differences are frequently ignored when analyzing the complexity of algorithms and implementations, as they appear to be insignificant in practice  In this paper, we demonstrate that this assumption can in fact have far more profound implications on time complexity than is obvious at first glance, and that a poor consideration of trade-offs can result in polynomially slower algorithms whose roots can be deeply and fundamentally ingrained into a programming language itself  While the general observation may not be novel from a theoretical standpoint, it is rarely (if ever) presented in traditional computer science curricula or other settings, and appears to be far from common knowledge in practical software engineering  We thus hope bring awareness to this issue and urge careful consideration of significant trade-offs that can result from trivial decisions made while programming.
Constant-factor differences are frequently ignored when analyzing the complexity of algorithms and implementations, as they appear to be insignificant in practice . In this paper, we demonstrate that this assumption can in fact have far more profound implications on time complexity than is obvious at first glance, and that a poor consideration of trade-offs can result in polynomially slower algorithms whose roots can be deeply and fundamentally ingrained into a programming language itself . While the general observation may not be novel from a theoretical standpoint, it is rarely (if ever) presented in traditional computer science curricula or other settings, and appears to be far from common knowledge in practical software engineering . We thus hope bring awareness to this issue and urge careful consideration of significant trade-offs that can result from trivial decisions made while programming.
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polynomially
polynomially
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