"The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming." (Donald E Knuth, "Computer Programming as an Art", 1968)
"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"The whole idea of a system is to optimize - not maximize - the fit of its elements in order to maximize the whole. If we merely maximize the elements of systems, we end up suboptimizing the whole [...]" (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)
"All feats of engineering, whether stone dwellings or space stations, require a particular sequence of events. First, the engineer must understand the needs and wants of the society or subgroup of society that is to be served. Second, the engineer must formulate concepts of potential designs that might serve the designated needs and wants. Third, the engineer must analyze the concepts to determine their functionality. Fourth, the engineer must optimize selected candidate designs and choose a single preferred design. And fifth, the engineer must design a production system to realize the selected design." (George A Hazelrigg, "Laws and Models: An Introduction", 2000)
"An optimization is said to be unsafe if it may lead to incorrect code in certain programs. It is said to be speculative if it usually improves performance, but may degrade it in certain cases. A compiler is said to be conservative if it applies optimizations only when it can guarantee that they will be both safe and effective. By contrast, an optimistic compiler may make liberal use of speculative optimizations. It may also pursue unsafe optimizations by generating two versions of the code, with a dynamic check that chooses between them based on information not available at compile time. Examples of speculative optimization include nonbinding prefetches, which try to bring data into the cache before they are needed, and trace scheduling, which rearranges code in hopes of improving the performance of the processor pipeline and the instruction cache." (Michael L Scott, "Programming Language Pragmatics" 3rd. Ed., 2009)
"Code improvement is often referred to as optimization, though it seldom makes anything optimal in any absolute sense. It is an optional phase of compilation whose goal is to transform a program into a new version that computes the same result more efficiently - more quickly or using less memory, or both." (Michael L Scott, "Programming Language Pragmatics" 3rd. Ed., 2009)
"An important thing to take away [...] is the approach we took to every optimization: profile the code to get a sense of what is going on, come up with a possible solution to fix slow parts, then profile to make sure the fix actually worked. Although this sounds straightforward, things can get complicated quickly [...]" (Micha Gorelick & Ian Ozsvald, "High Performance Python", 2014)
"Sometimes it’s good to be lazy. By profiling first, you can quickly identify the bottlenecks that need to be solved, and then you can solve just enough of these to achieve the performance you need. If you avoid profiling and jump to optimization, then it is quite likely that you’ll do more work in the long run. Always be driven by the results of profiling." (Micha Gorelick & Ian Ozsvald, "High Performance Python", 2014)
"[…] building for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. However, it’s also important to choose the right tool for the job, and different technologies each have their own strengths and weaknesses." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)
"Optimization is more than finding the best simulation results. It is itself a complex and evolving field that, subject to certain information constraints, allows data scientists, statisticians, engineers, and traders alike to perform reality checks on modeling results." (Chris Conlan, "Automated Trading with R: Quantitative Research and Platform Development", 2016)
"Engineering managers have a responsibility to optimize their teams. They improve engineering workflows and reduce dependencies and repetitive tasks. Self-sustaining teams minimize dependencies that hinder them in their efforts to achieve their objectives. Scalable teams minimize software delivery steps and eliminate bottlenecks. The mechanisms to achieve this may include the use of tools, conventions, documentation, processes, or abstract things such as values and principles. Any action that produces a tangible improvement in the speed, reliability, or robustness of your team’s work is worth your consideration." (Morgan Evans, "Engineering Manager's Handbook", 2023)
"The best performance improvement is the transition from the nonworking state to the working state." (John Ousterhout [attributed])
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