The project involves the implementation of classical optimization methods such as gradient descent and penalty methods, evolutionary algorithms such as genetic algorithm, particle swarm optimization, and ant colony optimization in the solution of optimization problems.

Optimization includes a class of methods to find global or local optima for discrete or continuous objectives; from evolutionary-based algorithms to swarm-based ones.

Analytical and numerical techniques like gradient descent, genetic algorithm, ... to solve a convex unconstrained nonlinear optimization problem from scratchh without using any python library

Convex Optimization Course and Playground.