Popular Optimization

This book uniquely explores popular optimization, presenting fundamental problems from diverse sources. It aims to cultivate a non-traditional problem-solving approach applicable to both optimization and broader contexts. The author introduces optimization models for well-known problems and proposes several new ones. Additionally, familiar problems from various fields are reformulated into optimization frameworks.

The book serves as a curated collection of intriguing problems, most of which have clear practical applications. For each problem, multiple solution techniques are provided, typically offering at least two distinct approaches. It also demonstrates how AI can assist readers in formulating and formalizing problems that require robust mathematical models. AI's benefits are highlighted throughout the decision-making process, from fine-tuning input data to effectively presenting results. The selected optimization problems span both continuous and discrete domains, including linear and nonlinear formulations.

The richness and depth of the content will be appreciated by both students and practitioners as they explore the variety of methods that can be applied to solve more complex, real-world problems.