convex function

A convex function is a function which features a single global minimum, whereas a non-convex functions presents many local minima.

A good analysis on the mathematical aspects and mathematical definition of convex and non-convex functions can be found at: https://rumn.medium.com/convex-vs-non-convex-functions-why-it-matters-in-optimization-for-machine-learning-39cd9427dfcc.

Some examples of ML algorithms which have restrictions related to convex functions are the following:

  • Gradient descent only works with convex functions (global minimum).
  • The MSE method works in linear regression but it does not work with logistic regression, because logistic regression creates a non-convex function.

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