"Term used to describe analytics that calculate and determine the most ideal scenario to meet a specific target. Optimization procedures analyze each scenario and supply a score. An optimization analytic can run through hundreds, even thousands, of scenarios and rank each one based on a target that is being achieved." (Brittany Bullard, "Style and Statistics", 2016)
"Optimization is the process of finding the most efficient algorithm for a given task." (Edward T Chen, "Deep Learning and Sustainable Telemedicine", 2020)
"A high learning rate lets the model adjust quickly but risks overshooting the optimal values and getting stuck in a cycle of jumping around without minimizing the error function. A low learning rate will give you better precision but could slow down training and increase computational cost, or it could get the model stuck in a local minimum, where it essentially loses sight of the big picture of what it is trying to accomplish. Choosing the right learning rate is something of a trade-off. It may take many adjustments to find the optimal learning rate if doing so by hand, but the job can also be made easier by optimization algorithms, which try to guess the optimal learning rate. Such methods include Bayesian optimizations and Grid Search, among others." (Robert Barton & Jerome Henry, "Demystifying Generative AI A Practical and Intuitive Introduction", 2026)
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