MARC Record from harvard_bibliographic_metadata

LEADER: 03217nam a22004575a 4500
001 013839713-9
005 20131206201211.0
008 100301s2004 xxu| s ||0| 0|eng d
020 $a9781402080999
020 $a9781402080999
020 $a9781402080982
024 7 $a10.1007/b130886$2doi
035 $a(Springer)9781402080999
040 $aSpringer
050 4 $aQA402.5-402.6
072 7 $aPBU$2bicssc
072 7 $aMAT003000$2bisacsh
082 04 $a519.6$223
100 1 $aJongen, Hubertus Th,$eauthor.
245 10 $aOptimization Theory /$cby Hubertus Th. Jongen, Klaus Meer, Eberhard Triesch.
264 1 $aBoston, MA :$bSpringer US,$c2004.
300 $aXI, 443 p.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
505 0 $aPartial contents: Preface -- PART I. CONTINUOUS OPTIMIZATION -- 1. Optimality Criteria on Simple Regions -- 2. Constraints, Lagrange Function, Optimality -- 3. Parametric Aspects, Semi-Infinite Optimization -- 4. Convex Functions, Duality, Separation Theorem -- 5. Linear Inequalities, Constraint Qualifications -- 6. Linear Programming: The Simplex Method -- 7. The Ellipsoid Method -- 8. Karmarkar’s Method for Linear Programming -- 9. Order of Convergence, Steepest Descent -- 10. Conjugate Direction, Variable Metric -- 11. Penalty-, Barrier-, Multiplier-, IP-Methods -- 12. Search Methods without Derivatives -- 13. One-Dimensional Minimization -- PART II. DISCRETE OPTIMIZATION -- 14. Graphs and Networks -- 15. Flows in Networks -- 16. Applications of the Max-Flow Min-Cut Theorem -- 17. Integer Linear Programming -- 18. Computability; the Turing machine -- 19. Complexity theory -- 20. Reducibility and NP-completeness -- 21. Some NP-completeness results -- 22. The Random Access Machine.
520 $aOptimization Theory is becoming a more and more important mathematical as well as interdisciplinary area, especially in the interplay between mathematics and many other sciences like computer science, physics, engineering, operations research, etc. This volume gives a comprehensive introduction into the theory of (deterministic) optimization on an advanced undergraduate and graduate level. One main feature is the treatment of both continuous and discrete optimization at the same place. This allows to study the problems under different points of view, supporting a better understanding of the entire field. Audience: The book can be adapted well as an introductory textbook into optimization theory on a basis of a two semester course; however, each of its parts can also be taught separately. Many exercises are included to increase the reader's understanding.
650 10 $aMathematics.
650 0 $aMathematics.
650 0 $aInformation theory.
650 0 $aComputational complexity.
650 0 $aMathematical optimization.
650 24 $aOptimization.
650 24 $aDiscrete Mathematics in Computer Science.
650 24 $aTheory of Computation.
700 1 $aTriesch, Eberhard,$eauthor.
700 1 $aMeer, Klaus,$eauthor.
776 08 $iPrinted edition:$z9781402080982
988 $a20131119
906 $0VEN