Screen LibraryOptimization in Engineering : Models and Algorithms /
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emp...
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| Main Authors: | |
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| Corporate Authors: | |
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| Published: |
Springer International Publishing : Imprint: Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Springer Optimization and Its Applications,
120 |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-319-56769-3 |
| Summary: |
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields. |
| Carrier Form: | 1 online resource(XV,412pages): illustrations. |
| ISBN: | 9783319567693 |
| Index Number: | QA402 |
| CLC: | O224-05 |
| Contents: | 1. Optimization is Ubiquitous -- 2. Linear Optimization -- 3. Mixed-Integer Linear Optimization -- 4. Nonlinear Optimization -- 5. Iterative Solution Algorithms for Nonlinear Optimization -- 6. Dynamic Optimization -- A. Taylor Approximations and Definite Matrices -- B. Convexity -- Index. |