Program optimization: Difference between revisions

Revision as of 11:52, 8 January 2025

Revision as of 04:58, 5 February 2025

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==Overview==

Although the term "optimization" is derived from "optimum",<ref>{{Cite book |lastlast1=Antoniou |firstfirst1=Andreas |url=https://link.springer.com/content/pdf/10.1007/978-1-0716-0843-2.pdf |title=Practical Optimization |last2=Lu |first2=Wu-Sheng |series=Texts in Computer Science |publisher=[[Springer Publishing|Springer]] |year=2021 |edition=2nd |pages=1 |doi=10.1007/978-1-0716-0843-2 |isbn=978-1-0716-0841-8 |language=en}}</ref> achieving a truly optimal system is rare in practice, which is referred to as [[superoptimization]]. Optimization typically focuses on improving a system with respect to a specific quality metric rather than making it universally optimal. This often leads to trade-offs, where enhancing one metric may come at the expense of another. One popular example is [[space-time tradeoff]], reducing a program’s execution time by increasing its memory consumption. Conversely, in scenarios where memory is limited, engineers might prioritize a slower [[algorithm]] to conserve space. There is rarely a single design that can excel in all situations, requiring [[engineers]] to prioritize attributes most relevant to the application at hand.

Furthermore, achieving absolute optimization often demands disproportionate effort relative to the benefits gained. Consequently, optimization processes usually stop once sufficient improvements are achieved, without striving for perfection. Fortunately, significant gains often occur early in the optimization process, making it practical to stop before reaching [[diminishing returns]].