What is Optimization? + Learning Gradient Descent  Two Minute Papers #82  Summary and Q&A
TL;DR
Mathematical optimization is a technique used to find the optimal solution to a problem by adjusting variables and minimizing or maximizing an objective function.
Questions & Answers
Q: What is mathematical optimization?
Mathematical optimization is a technique that involves adjusting variables to find the best possible solution to a problem by minimizing or maximizing an objective function.
Q: How is optimization used in different fields?
Optimization is widely used in various fields like computer science, engineering, and deep learning to solve complex problems and improve efficiency.
Q: What is gradient descent?
Gradient descent is a simple optimization algorithm that involves adjusting variables and finding the direction that leads to the most favorable changes in the objective function.
Q: Can optimization algorithms be learned?
Yes, the DeepMind paper shows that optimization algorithms can emerge as a result of learning and can outperform previously existing methods on specialized problems.
Summary & Key Takeaways

Mathematical optimization involves finding the best possible solution by adjusting variables and optimizing an objective function.

Optimization is used in various fields like computer science, engineering, and deep learning.

Gradient descent is a popular optimization algorithm that involves making small changes to variables to find the most favorable results.