Constrained Optimization demystified -- with implementation in Python.

Nonlinear constrained optimization problems are an important class of problems with a broad range of engineering, and scientific applications. In this article, we will see how the refashioning of simple unconstrained Optimization techniques leads to a hybrid algorithm for constrained optimization problems. Later, we will observe the robustness of the algorithm through a detailed analysis of a problem set and monitor the performance of optima by comparing the results with some of the inbuilt functions in python. Many engineering design and decision making problems have an objective of optimizing a function and simultaneously have a requirement for satisfying some constraints arising due to space, strength, or stability considerations. So, Constrained optimization refers to the process of optimizing an objective function with respect to some variables in the presence of constraint of those variables.