Optimization Guide

This is the main note for my Optimizations Guide.

This note gives a geometric, engineer-friendly view of nonlinear optimization and shows how the Lagrangian turns “constraints plus objective” into a single analytical object. Along the way, it builds up the ideas of Lagrange multipliers and KKT conditions step by step, so you can see them as sensitivity measures and practical optimality checks rather than abstract math.