Linear Programming
Linear programming is a mathematical optimization technique used to find the best outcome (such as maximum profit or minimum cost) in a mathematical model whose requirements are represented by linear relationships. It involves maximizing or minimizing a linear objective function subject to a set of linear equality or inequality constraints, typically applied in operations research, economics, and engineering. The solution is often found using algorithms like the simplex method or interior-point methods.
Developers should learn linear programming when building systems that require optimal resource allocation, such as supply chain optimization, scheduling, financial portfolio management, or network flow problems. It is essential for solving complex decision-making problems in data science, machine learning (e.g., support vector machines), and industrial applications where efficiency and cost-effectiveness are critical.