Approximation and Complexity in Numerical Optimization: Continuous and Discrete Problems
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA
Springer US
2000
|
| Series: | Nonconvex Optimization and Its Applications
42 |
| Subjects: | |
| Links: | https://doi.org/10.1007/978-1-4757-3145-3 |
| Item Description: | There has been much recent progress in approximation algorithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. In discrete (or combinatorial) optimization many approaches have been developed recently that link the discrete universe to the continuous universe through geomet ric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. As a result new ap proximate algorithms have been discovered and many new computational approaches have been developed. Similarly, for many continuous nonconvex optimization prob lems, new approximate algorithms have been developed based on semidefinite pro gramming and new randomization techniques. On the other hand, computational complexity, originating from the interactions between computer science and numeri cal optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty. The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are likely to be tractable. The quest for developing efficient algorithms leads also to elegant general approaches for solving optimization problems, and reveals surprising connections among problems and their solutions. A conference on Approximation and Complexity in Numerical Optimization: Con tinuous and Discrete Problems was held during February 28 to March 2, 1999 at the Center for Applied Optimization of the University of Florida |
| Physical Description: | 1 Online-Ressource (XIV, 581 p) |
| ISBN: | 9781475731453 9781441948298 |
| ISSN: | 1571-568X |
| DOI: | 10.1007/978-1-4757-3145-3 |
Staff View
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| 500 | |a There has been much recent progress in approximation algorithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. In discrete (or combinatorial) optimization many approaches have been developed recently that link the discrete universe to the continuous universe through geomet ric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. As a result new ap proximate algorithms have been discovered and many new computational approaches have been developed. Similarly, for many continuous nonconvex optimization prob lems, new approximate algorithms have been developed based on semidefinite pro gramming and new randomization techniques. On the other hand, computational complexity, originating from the interactions between computer science and numeri cal optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty. The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are likely to be tractable. The quest for developing efficient algorithms leads also to elegant general approaches for solving optimization problems, and reveals surprising connections among problems and their solutions. A conference on Approximation and Complexity in Numerical Optimization: Con tinuous and Discrete Problems was held during February 28 to March 2, 1999 at the Center for Applied Optimization of the University of Florida | ||
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Record in the Search Index
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| illustrated | Not Illustrated |
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| institution | BVB |
| isbn | 9781475731453 9781441948298 |
| issn | 1571-568X |
| language | English |
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| publisher | Springer US |
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| series2 | Nonconvex Optimization and Its Applications |
| spellingShingle | Pardalos, Panos M. Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems Mathematics Information theory Mathematical optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Approximations and Expansions Mathematik Optimierung (DE-588)4043664-0 gnd Berechnungskomplexität (DE-588)4134751-1 gnd Approximation (DE-588)4002498-2 gnd Numerisches Verfahren (DE-588)4128130-5 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4134751-1 (DE-588)4002498-2 (DE-588)4128130-5 (DE-588)1071861417 |
| title | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems |
| title_auth | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems |
| title_exact_search | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems |
| title_full | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems edited by Panos M. Pardalos |
| title_fullStr | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems edited by Panos M. Pardalos |
| title_full_unstemmed | Approximation and Complexity in Numerical Optimization Continuous and Discrete Problems edited by Panos M. Pardalos |
| title_short | Approximation and Complexity in Numerical Optimization |
| title_sort | approximation and complexity in numerical optimization continuous and discrete problems |
| title_sub | Continuous and Discrete Problems |
| topic | Mathematics Information theory Mathematical optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Approximations and Expansions Mathematik Optimierung (DE-588)4043664-0 gnd Berechnungskomplexität (DE-588)4134751-1 gnd Approximation (DE-588)4002498-2 gnd Numerisches Verfahren (DE-588)4128130-5 gnd |
| topic_facet | Mathematics Information theory Mathematical optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Approximations and Expansions Mathematik Optimierung Berechnungskomplexität Approximation Numerisches Verfahren Konferenzschrift 1999 Gainesville Fla. Konferenzschrift |
| url | https://doi.org/10.1007/978-1-4757-3145-3 |
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