Maxima and minima

Level FundamentalDifficulty ★★★★★Concept⌖ Open in the map

What is it?

A global minimum is the lowest value of ff anywhere; a local minimum is lowest only in a neighbourhood. In optimization the difference between the two is the difference between a good model and a stuck one.

Formulas

f(x∗)≤f(x)  ∀xf(x^\ast) \le f(x)\ \ \forall x
x∗x^\ast is a global minimum
∃δ>0: f(x∗)≤f(x)  ∀x, ∣x−x∗∣<δ\exists\delta > 0:\ f(x^\ast) \le f(x)\ \ \forall x,\ |x - x^\ast| < \delta
x∗x^\ast is a local minimum

Where it shows up in computing

  • Operations research and logistics★★★★★fundamentalOptimization and systems

    Cost minimization and profit maximization are the native language of operations research.

Where it shows up in AI

  • Loss function★★★★★fundamentalAI and machine learning

    Training is the search for parameters that minimize the loss — in practice, a good local minimum.

Where is it used?

Computing topics reachable from here, through the chain of ideas that leads to them:

What depends on it

This page has the essentials. A fuller treatment (intuition, formal definition, worked example) is on the way.

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