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Viser: Practical Mathematical Optimization - Basic Optimization Theory and Gradient-Based Algorithms
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Practical Mathematical Optimization Vital Source e-bog
Jan A Snyman
(2018)
Springer Nature
350,00 kr.
Leveres umiddelbart efter køb
Practical Mathematical Optimization Vital Source e-bog
Jan A Snyman og Daniel N Wilke
(2018)
Springer Nature
175,00 kr.
Leveres umiddelbart efter køb
Practical Mathematical Optimization Vital Source e-bog
Jan A Snyman og Daniel N Wilke
(2018)
Springer Nature
227,00 kr.
Leveres umiddelbart efter køb
Practical Mathematical Optimization Vital Source e-bog
Jan A Snyman og Daniel N Wilke
(2018)
Springer Nature
350,00 kr.
Leveres umiddelbart efter køb
Practical Mathematical Optimization
Basic Optimization Theory and Gradient-Based Algorithms
Jan Snyman og Daniel Nicolas Wilke
(2018)
Sprog: Engelsk
Springer International Publishing AG
846,00 kr.
Print on demand. Leveringstid vil være ca 2-3 uger.
Detaljer om varen
- 2. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Springer Nature (Maj 2018)
- ISBN: 9783319775869
This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 2. Udgave
- Vital Source 90 day rentals (dynamic pages)
- Udgiver: Springer Nature (Maj 2018)
- Forfattere: Jan A Snyman og Daniel N Wilke
- ISBN: 9783319775869R90
This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills.
Licens varighed:
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 2. Udgave
- Vital Source 180 day rentals (dynamic pages)
- Udgiver: Springer Nature (Maj 2018)
- Forfattere: Jan A Snyman og Daniel N Wilke
- ISBN: 9783319775869R180
This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills.
Licens varighed:
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 2. Udgave
- Vital Source 365 day rentals (dynamic pages)
- Udgiver: Springer Nature (Maj 2018)
- Forfattere: Jan A Snyman og Daniel N Wilke
- ISBN: 9783319775869R365
This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills.
Licens varighed:
Bookshelf online: 365 dage fra købsdato.
Bookshelf appen: 365 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 365 dage fra købsdato.
Bookshelf appen: 365 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 2. Udgave
- Hardback
- Udgiver: Springer International Publishing AG (Maj 2018)
- Forfattere: Jan Snyman og Daniel Nicolas Wilke
- ISBN: 9783319775852
This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.
1.Introduction.-
2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems.-
5. Some Basic Optimization Theorems.-
6. New gradient-based trajectory and approximation methods.-
7. Surrogate Models.-
8. Gradient-only solution strategies.-
9. Practical computational optimization using Python.- Appendix.- Index.
2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems.-
5. Some Basic Optimization Theorems.-
6. New gradient-based trajectory and approximation methods.-
7. Surrogate Models.-
8. Gradient-only solution strategies.-
9. Practical computational optimization using Python.- Appendix.- Index.
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