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Introduction to algorithms
Abstract
Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.
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Cited by
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Jeong, G., Kim, S., & Zimmermann, T. (2009) · Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering · doi:10.1145/1595696.1595715 · Cited by 455
Metadata
- Venue
- Optimization Models
- Publisher
- Cambridge University Press
- Published
- 2018-05-28
- Pages
- 425-502
- OpenAlex
- W4210727445
- Cited by
- 818
- References
- 0
- FWCI
- 116.73
- Type
- book chapter
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
