|
|
|
|
| LEADER |
03350nmm a2200409 u 4500 |
| 001 |
EB000632187 |
| 003 |
EBX01000000000000000485269 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
140122 ||| eng |
| 020 |
|
|
|a 9781475757958
|
| 100 |
1 |
|
|a Ding-Zhu Du
|
| 245 |
0 |
0 |
|a Mathematical Theory of Optimization
|h Elektronische Ressource
|c by Ding-Zhu Du, Panos M. Pardalos, Weili Wu
|
| 250 |
|
|
|a 1st ed. 2001
|
| 260 |
|
|
|a New York, NY
|b Springer US
|c 2001, 2001
|
| 300 |
|
|
|a XIII, 273 p
|b online resource
|
| 505 |
0 |
|
|a 1 Optimization Problems -- 2 Linear Programming -- 3 Blind Man’s Method -- 4 Hitting Walls -- 5 Slope and Path Length -- 6 Average Slope -- 7 Inexact Active Constraints -- 8 Efficiency -- 9 Variable Metric Methods -- 10 Powell’s Conjecture -- 11 Minimax -- 12 Relaxation -- 13 Semidefinite Programming -- 14 Interior Point Methods -- 15 From Local to Global -- Historical Notes
|
| 653 |
|
|
|a Software engineering
|
| 653 |
|
|
|a Optimization
|
| 653 |
|
|
|a Computer science
|
| 653 |
|
|
|a Software Engineering
|
| 653 |
|
|
|a Algorithms
|
| 653 |
|
|
|a Computational Mathematics and Numerical Analysis
|
| 653 |
|
|
|a Mathematics / Data processing
|
| 653 |
|
|
|a Applications of Mathematics
|
| 653 |
|
|
|a Theory of Computation
|
| 653 |
|
|
|a Mathematics
|
| 653 |
|
|
|a Mathematical optimization
|
| 700 |
1 |
|
|a Pardalos, Panos M.
|e [author]
|
| 700 |
1 |
|
|a Weili Wu
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b SBA
|a Springer Book Archives -2004
|
| 490 |
0 |
|
|a Nonconvex Optimization and Its Applications
|
| 028 |
5 |
0 |
|a 10.1007/978-1-4757-5795-8
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4757-5795-8?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 519.6
|
| 520 |
|
|
|a Optimization is of central importance in all sciences. Nature inherently seeks optimal solutions. For example, light travels through the "shortest" path and the folded state of a protein corresponds to the structure with the "minimum" potential energy. In combinatorial optimization, there are numerous computationally hard problems arising in real world applications, such as floorplanning in VLSI designs and Steiner trees in communication networks. For these problems, the exact optimal solution is not currently real-time computable. One usually computes an approximate solution with various kinds of heuristics. Recently, many approaches have been developed that link the discrete space of combinatorial optimization to the continuous space of nonlinear optimization through geometric, analytic, and algebraic techniques. Many researchers have found that such approaches lead to very fast and efficient heuristics for solving large problems. Although almost all such heuristics work well in practice there is no solid theoretical analysis, except Karmakar's algorithm for linear programming. With this situation in mind, we decided to teach a seminar on nonlinear optimization with emphasis on its mathematical foundations. This book is the result of that seminar. During the last decades many textbooks and monographs in nonlinear optimization have been published. Why should we write this new one? What is the difference of this book from the others? The motivation for writing this book originated from our efforts to select a textbook for a graduate seminar with focus on the mathematical foundations of optimization
|