👉 Optimization math involves finding the best solution from a set of possible solutions, often by minimizing or maximizing a specific function subject to constraints. This process typically uses techniques like calculus (derivatives and gradients), linear algebra (vector spaces and matrices), and optimization algorithms (gradient descent, Newton's method). The goal is to identify the point where the objective function reaches its maximum or minimum value while adhering to given constraints, such as resource limits or physical laws. For example, in machine learning, optimization math helps train models by adjusting parameters to minimize prediction errors, using gradient descent to iteratively move towards the lowest error surface.
