An Introduction to Mathematical Programming and Network Science

This text provides a practical, hands-on introduction to the fundamental concepts of mathematical programming and network science. Particular emphasis is placed on linear programming, mathematical modelling and case studies, the implementation of the Simplex Method in Python, and classical techniques from nonlinear convex programming. The text also features a discussion of mathematical programming within the context of algebraic modelling languages.  Further, it includes material on matrix games, decision analysis, multicriteria optimization and non-directed networks. Designed as an introductory resource for upper-level undergraduate and graduate students, the book assumes only a modest mathematical background. Readers who have completed a second course in linear algebra, multivariable calculus, and an introductory course in probability and statistics will find the more advanced portions of the text especially accessible. Researchers and professionals in mathematics, engineering, technology, economics, business, and other quantitatively oriented fields will also find this book a valuable reference.A distinguishing feature of this text is its strong emphasis on case studies. Numerous examples are developed in detail, either worked out within the text or explored through exercises and abstract model formulations. This pedagogical approach fosters both intuition and a structured understanding of the representative models that form the foundation of the field. A rich collection of end-of-chapter exercises enables readers to apply concepts and deepen their mastery of the material. A chapter dependency chart further supports independent learners by suggesting an effective study sequence and assists instructors in organizing coherent course structures.

This text provides a practical, hands-on introduction to the fundamental concepts of mathematical programming and network science. Particular emphasis is placed on linear programming, mathematical modelling and case studies, the implementation of the Simplex Method in Python, and classical techniques from nonlinear convex programming. The text also features a discussion of mathematical programming within the context of algebraic modelling languages.  Further, it includes material on matrix games, decision analysis, multicriteria optimization and non-directed networks. Designed as an introductory resource for upper-level undergraduate and graduate students, the book assumes only a modest mathematical background. Readers who have completed a second course in linear algebra, multivariable calculus, and an introductory course in probability and statistics will find the more advanced portions of the text especially accessible. Researchers and professionals in mathematics, engineering, technology, economics, business, and other quantitatively oriented fields will also find this book a valuable reference.A distinguishing feature of this text is its strong emphasis on case studies. Numerous examples are developed in detail, either worked out within the text or explored through exercises and abstract model formulations. This pedagogical approach fosters both intuition and a structured understanding of the representative models that form the foundation of the field. A rich collection of end-of-chapter exercises enables readers to apply concepts and deepen their mastery of the material. A chapter dependency chart further supports independent learners by suggesting an effective study sequence and assists instructors in organizing coherent course structures.