Optimization, also known as mathematical programming, is a collection of mathematical principles and methods used for solving quantitative problems in various disciplines, including physics, biology, engineering, economics, and business. This field emerged from the realization that quantitative problems in seemingly different domains share common mathematical elements. Consequently, many problems can be effectively formulated and solved using the unified set of ideas and methods that comprise the realm of optimization.

This repository is dedicated to showcasing my implementations across various branches of mathematical optimization.

• CVX Opt: This section contains implementations related to convex optimization problems. Most of these implementations are based on exercises from the Convex Optimization textbook by Stephen P. Boyd. For in-depth information, please refer to the CVX OPT Readme.

• Numerical Opt: Here, you'll find implementations of numerical optimization methods, with many of them based on algorithms outlined in the Numerical Optimization textbook by Jorge Nocedal and J. Wright. Additionally, this section includes implementations of recent optimization papers. Explore further in the Numerical Optimization Readme.

• Game Theory: In this section, you can explore implementations related to game theory approaches applied to various problems. Gain deeper insights by referring to the Game Theory Readme.

By exploring these sections, you'll delve into a world of mathematical optimization, gaining practical experience in solving complex problems across different domains. If you're interested in specific algorithms or have questions related to any of these topics, please feel free to reach out for further information or assistance.