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Filling the need for an introductory book on linear programming that addresses parameter uncertainty, this text offers a concrete and intuitive introduction to modern linear optimization. It covers fundamental topics and current technologies, including predictor-path following interior point methods for linear and quadratic optimization. The book emphasizes stochastic programming with recourse and robust optimization as frameworks for managing parameter uncertainty, underscoring their significance in decision-making processes. By introducing these concepts early, the author enhances the reader's ability to make informed decisions in real-world scenarios. Applications and case studies from finance and supply chain management, utilizing MATLAB, are included to illustrate practical use. Unlike many existing LP texts that focus on MS Excel and overlook data uncertainty, this book prioritizes MATLAB, a preferred tool for engineers, including financial engineers. It rigorously develops state-of-the-art methods for addressing parameter uncertainty in linear programming while ensuring that intuition precedes theory, making the material engaging and accessible. This approach not only highlights the relevance of the topics but also prepares readers to tackle real-world challenges effectively.
Acquisto del libro
Introduction to Linear Optimization and Extensions with MATLAB, Roy H. Kwon
- Lingua
- Pubblicato
- 2013
- product-detail.submit-box.info.binding
- (Copertina rigida)
Metodi di pagamento
Ancora nessuna valutazione.
- Titolo
- Introduction to Linear Optimization and Extensions with MATLAB
- Lingua
- Inglese
- Autori
- Roy H. Kwon
- Editore
- CRC Press
- Pubblicato
- 2013
- Formato
- Copertina rigida
- Pagine
- 362
- ISBN10
- 143986263X
- ISBN13
- 9781439862636
- Serie
- Descrizione
- Filling the need for an introductory book on linear programming that addresses parameter uncertainty, this text offers a concrete and intuitive introduction to modern linear optimization. It covers fundamental topics and current technologies, including predictor-path following interior point methods for linear and quadratic optimization. The book emphasizes stochastic programming with recourse and robust optimization as frameworks for managing parameter uncertainty, underscoring their significance in decision-making processes. By introducing these concepts early, the author enhances the reader's ability to make informed decisions in real-world scenarios. Applications and case studies from finance and supply chain management, utilizing MATLAB, are included to illustrate practical use. Unlike many existing LP texts that focus on MS Excel and overlook data uncertainty, this book prioritizes MATLAB, a preferred tool for engineers, including financial engineers. It rigorously develops state-of-the-art methods for addressing parameter uncertainty in linear programming while ensuring that intuition precedes theory, making the material engaging and accessible. This approach not only highlights the relevance of the topics but also prepares readers to tackle real-world challenges effectively.