Handbook of Combinatorial Optimization: Supplement Volume A
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1999
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied mathematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, airline crew scheduling, corporate planning, computer-aided design and manufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, allocation of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discoveries, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algorithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addition, linear programming relaxations are often the basis for many approximation algorithms for solving NP-hard problems (e.g. dual heuristics) |
| Beschreibung: | 1 Online-Ressource (VIII, 648 p) |
| ISBN: | 9781475730234 9781441948137 |
| DOI: | 10.1007/978-1-4757-3023-4 |
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| 500 | |a Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied mathematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, airline crew scheduling, corporate planning, computer-aided design and manufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, allocation of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discoveries, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algorithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addition, linear programming relaxations are often the basis for many approximation algorithms for solving NP-hard problems (e.g. dual heuristics) | ||
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Information theory | |
| 650 | 4 | |a Computational complexity | |
| 650 | 4 | |a Combinatorics | |
| 650 | 4 | |a Discrete Mathematics in Computer Science | |
| 650 | 4 | |a Theory of Computation | |
| 650 | 4 | |a Information and Communication, Circuits | |
| 650 | 4 | |a Mathematik | |
| 700 | 1 | |a Du, Ding-Zhu |4 edt | |
| 700 | 1 | |a Pardalos, Panos M. |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| bvnumber | BV042421409 |
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| dewey-full | 511.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 511 - General principles of mathematics |
| dewey-raw | 511.6 |
| dewey-search | 511.6 |
| dewey-sort | 3511.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-3023-4 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:08Z |
| institution | BVB |
| isbn | 9781475730234 9781441948137 |
| language | English |
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| physical | 1 Online-Ressource (VIII, 648 p) |
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| spelling | Handbook of Combinatorial Optimization Supplement Volume A edited by Ding-Zhu Du, Panos M. Pardalos Boston, MA Springer US 1999 1 Online-Ressource (VIII, 648 p) txt rdacontent c rdamedia cr rdacarrier Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied mathematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, airline crew scheduling, corporate planning, computer-aided design and manufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, allocation of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discoveries, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algorithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addition, linear programming relaxations are often the basis for many approximation algorithms for solving NP-hard problems (e.g. dual heuristics) Mathematics Information theory Computational complexity Combinatorics Discrete Mathematics in Computer Science Theory of Computation Information and Communication, Circuits Mathematik Du, Ding-Zhu edt Pardalos, Panos M. Sonstige oth https://doi.org/10.1007/978-1-4757-3023-4 Verlag Volltext |
| spellingShingle | Handbook of Combinatorial Optimization Supplement Volume A Mathematics Information theory Computational complexity Combinatorics Discrete Mathematics in Computer Science Theory of Computation Information and Communication, Circuits Mathematik |
| title | Handbook of Combinatorial Optimization Supplement Volume A |
| title_auth | Handbook of Combinatorial Optimization Supplement Volume A |
| title_exact_search | Handbook of Combinatorial Optimization Supplement Volume A |
| title_full | Handbook of Combinatorial Optimization Supplement Volume A edited by Ding-Zhu Du, Panos M. Pardalos |
| title_fullStr | Handbook of Combinatorial Optimization Supplement Volume A edited by Ding-Zhu Du, Panos M. Pardalos |
| title_full_unstemmed | Handbook of Combinatorial Optimization Supplement Volume A edited by Ding-Zhu Du, Panos M. Pardalos |
| title_short | Handbook of Combinatorial Optimization |
| title_sort | handbook of combinatorial optimization supplement volume a |
| title_sub | Supplement Volume A |
| topic | Mathematics Information theory Computational complexity Combinatorics Discrete Mathematics in Computer Science Theory of Computation Information and Communication, Circuits Mathematik |
| topic_facet | Mathematics Information theory Computational complexity Combinatorics Discrete Mathematics in Computer Science Theory of Computation Information and Communication, Circuits Mathematik |
| url | https://doi.org/10.1007/978-1-4757-3023-4 |
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