Python Optimization Algorithms in Practice

Name: Python Optimization Algorithms in Practice
ISBN: 9787301315330

苏振裕Peking University Press · ChinaAug 2025

3.3

Word count: 127 words
Chapters: 14
ISBN: 9787301315330

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Summary

Combining theory with hands-on programming, this book uses Python to explain the principles and applications of optimization algorithms, covering Python fundamentals, the Gurobi optimizer, linear programming, integer programming, multi-objective optimization, dynamic programming, graph and network analysis, and intelligent optimization algorithms.

Editor's review

Guided by the principle of combining theory with programming practice, this book integrates the explanation of optimization algorithm principles with Python implementations into a single framework, avoiding the common disconnect of presenting only formulas or only code. It covers a broad range of topics, from Python fundamentals and the Gurobi optimizer to linear programming, integer programming, multi-objective optimization, dynamic programming, graph and network analysis, and intelligent optimization algorithms, forming an algorithm spectrum that progresses from basic to advanced. Emphasizing hands-on practice, the book enables readers to master solver invocation and algorithm coding while understanding model construction, making it suitable as a course companion or for self-study. Readers are advised to type along with the code, run the examples first, then try modifying constraints and objective functions, gradually building an awareness of the complete workflow from problem modeling to solution verification.

Who it's for

A good fit for

University students who need to apply optimization methods to coursework or research modeling
Developers with some programming background who want to systematically learn optimization algorithm implementation
Technical professionals engaged in operations research optimization and data analysis who need to quickly get started with Python tools

Not a good fit for

Readers with no programming experience who do not intend to write code
Readers of popular science who only want to understand optimization concepts and are not interested in algorithm implementation details

Reading tips

First skim through the Python fundamentals chapter to ensure you can run the sample code independently.
Proceed in the order of linear programming, integer programming, and dynamic programming, reproducing the cases in each chapter by hand.
After finishing each type of algorithm, rewrite the model with your own small dataset and compare the solution results.
For chapters involving the Gurobi optimizer, confirm licensing and set up the installation environment before starting.

About the publisher

Peking University Press China · Founded in 1979

Peking University Press is a comprehensive university press whose publishing areas cover classical literature, popular science, social culture, world history, Western philosophy, and culture. Its catalogue contains 6,031 titles, with representative books including A Dream of Red Mansions, Relativity: The Special and General Theory (15th Edition) (with Einstein's classic papers on unified field theory and other topics), Elements, Conics, The Aristocratic Families of the Eastern Jin, The Philosophy of Mythology (Volume I), The Philosophy of Mythology (Volume II), and The Origin of Continents and Oceans. Representative authors include Cao Xueqin, Zhou Ruchang, Albert Einstein, Euclid, Apollonius, Tian Yuqing, Schelling, and Xian Gang. The press has developed distinctive strengths in classical Chinese literature and in the translation of Western philosophical classics, while also covering scientific classics and social and cultural studies. As public information is limited, this description is based only on the books in the catalogue and offers no further extrapolation.

版权信息
前言
第1篇最优化算法与编程基础
第1章最优化算法概述
第2章 Python编程方法
第3章 Gurobi优化器
第2篇数学规划方法
第4章线性规划
第5章整数规划
第6章多目标优化
第3篇启发式算法
第7章动态规划
第8章图与网络分析
第9章智能优化算法

FAQ

Can I read this book directly without a background in operations research?

You can read it, but you will need to fill in some background. The book focuses on combining principles with programming; without basic concepts such as linear programming and integer programming, understanding model construction will be difficult. It is recommended to first learn basic terms like objective function, constraints, and feasible solutions, then learn and practice alongside the book's code, which will work much better.

Does the Gurobi optimizer in the book require payment?

Gurobi is a commercial optimization solver that generally requires a license for full functionality, and typically offers trial or academic licensing channels. Before starting, readers should confirm their available licensing method and complete environment setup to avoid learning progress being affected by the solver being unavailable. For specific licensing policies, refer to the official documentation.

What mathematical background is needed for the intelligent optimization algorithms section?

The intelligent optimization algorithms chapter involves concepts such as iteration, fitness, and probabilistic search; basic knowledge of higher mathematics and probability and statistics is sufficient to follow along. The focus is not on deriving complex theorems but on understanding algorithm workflows and implementing them in Python. When encountering unfamiliar mathematical expressions, you can understand them in reverse by combining them with code execution results.

Is this book more suitable as a textbook or for self-study?

Both. As a textbook, its chapters cover a complete chain from fundamentals to multiple types of algorithms, making it easy to progress through a semester; as a self-study book, the sample code and solver explanations provide an actionable path. The key is to maintain a hands-on rhythm, running and modifying the code after each section, and avoid reading without practicing.

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About the author

苏振裕 Author

Su Zhenyu is an author for whom publicly available information is limited: his nationality, dates of birth and death, and educational background are not clearly recorded, and the library holds only one of his works. Based on the available information, Su Zhenyu's writing focuses on the field of programming and design, and his representative work is Python Optimization Algorithms in Practice. The book uses the Python language as its medium and centers on the implementation and application of optimization algorithms, making it a technical work oriented toward practice. Works of this kind are usually aimed at readers with some programming background and emphasize turning algorithmic principles into runnable code rather than dwelling on theoretical derivation. Because biographical material about the author is scarce, it is difficult to make further judgments about the overall character of his writing style; nevertheless, the title and classification suggest that Su Zhenyu is concerned with combining programming tools with algorithmic methods, and that his work is characterized by a practical orientation among technical books of its kind.