



LEADER 
03350nmm a2200409 u 4500 
001 
EB000632187 
003 
EBX01000000000000000485269 
005 
00000000000000.0 
007 
cr 
008 
140122  eng 
020 


a 9781475757958

100 
1 

a DingZhu Du

245 
0 
0 
a Mathematical Theory of Optimization
h Elektronische Ressource
c by DingZhu 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 6392

989 


b SBA
a Springer Book Archives 2004

490 
0 

a Nonconvex Optimization and Its Applications

028 
5 
0 
a 10.1007/9781475757958

856 
4 
0 
u https://doi.org/10.1007/9781475757958?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 realtime 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
