- [[economics is constrained optimization|economics is constrained optimization]] - [[machine-learning algorithms]], [[gradient-based optimization algorithms]], [[gradient descent]], [[genetic algorithms]] - central to [[deep learning]] - [[scipy.optimize.minimize]] # Idea Optimization is about finding maximum or minimum values. It allows us to find the optimal solution to problems that can be described as mathematical functions. [[Pierre de Fermat]] was one of the first to solve optimization problems—before [[calculus]] and [[derivative|derivatives]] were invented. He used the [[double intersection]] method. ## Applications Given the economics is all about [[economics is constrained optimization]], optimality is the cornerstone of standard economic theory. # References - [youtube using scipy.optimize.minimize](https://www.youtube.com/watch?v=wS5D72wLez8) - [youtube scipy beginners guide](https://www.youtube.com/watch?v=cXHvC_FGx24) - https://campus.datacamp.com/courses/linear-classifiers-in-python/loss-functions?ex=4 - https://courses.kristakingmath.com/library/applications-of-derivatives-dc8b34ad/110495/path/step/57969616/ - [Optimizer Selection — Bumps 0.9.0 documentation](https://bumps.readthedocs.io/en/latest/guide/optimizer.html) ![[least squares or squared loss#Minimizing loss in Python with scipy optimize minimize]]