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140122  eng 
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a 9781461334378

100 
1 

a Floudas, Christodoulos A.
e [editor]

245 
0 
0 
a State of the Art in Global Optimization
h Elektronische Ressource
b Computational Methods and Applications
c edited by Christodoulos A. Floudas, Panos M. Pardalos

250 


a 1st ed. 1996

260 


a New York, NY
b Springer US
c 1996, 1996

300 


a X, 654 p
b online resource

505 
0 

a Test Results for an Interval Branch and Bound Algorithm for EqualityConstrained Optimization  Equivalent Methods for Global Optimization  A C++ Class Library for Interval Arithmetic in Global Optimization  On the Convergence of Localisation Search  Stochastic Approximation with Smoothing for Optimization of an Adaptive Recursive Filter  The Grouping Genetic Algorithm  Accelerating Convergence of BranchandBound Algorithms for Quadratically Constrained Optimization Problems  Distributed DecompositionBased Approaches in Global Optimization  A Finite Algorithm for Global Minimization of Separable Concave Programs  A Pseudo ?Approximate Algorithm for Feedback Vertex Set  Iterative Topographical Global Optimization  Global Optimization for the Chemical and Phase Equilibrium Problem using Interval Analysis  Nonconvex Global Optimization of the Separable ResourceAllocation Problem with Continuous Variables 

505 
0 

a A. D.C. Approach to the Largest Empty Sphere Problem in Higher Dimension  A General D.C. Approach to Location Problems  Global Optimization by Parallel Constrained Biased Random Search  Global Optimization Problems in Computer Vision  An Application of Optimization to the Problem of Climate Change  Dynamic Visualization in Modelling and Optimization of Ill Defined Problems  A New Global Optimization Algorithm for Batch Process Scheduling  Nonconvexity and Descent in Nonlinear Programming  Global Optimization of Chemical Processes using Stochastic Algorithms  LogicBased OuterApproximation and Benders Decomposition Algorithms for the Synthesis of Process Networks  Combinatorially Accelerated BranchandBound Method for Solving the MIP Model of Process Network Synthesis  Discrete Optimization using String Encodings for the Synthesis of Complete Chemical Processes

505 
0 

a Lagrange Duality in Partly Convex Programming  Global Optimization using Hyperbolic Cross Points  Global Minimization of Separable Concave Functions under Linear Constraints with Totally Unimodular Matrices  On Existence of Robust Minimizers  A Branch and Bound Algorithm for the Quadratic Assignment Problem using a Lower Bound Based on Linear Programming  Dynamic Matrix Factorization Methods for using Formulations Derived from Higher Order Lifting Techniques in the Solution of the Quadratic Assignment Problem  Conical Coercivity Conditions and Global Minimization on Cones. An Existence Result  The use of Ordinary Differential Equations in Quadratic Maximization with Integer Constraints  Adaptive Control via NonConvex Optimization  A DecompositionBased Global Optimization Approach for Solving Bilevel Linear and Quadratic Problems  Generalized TRUST Algorithms for Global Optimization 

653 


a Engineering

653 


a Control theory

653 


a Systems Theory, Control

653 


a System theory

653 


a Chemistry, Technical

653 


a Mathematical Modeling and Industrial Mathematics

653 


a Technology and Engineering

653 


a Industrial Chemistry

653 


a Mathematical models

700 
1 

a Pardalos, Panos M.
e [editor]

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 10.1007/9781461334378

856 
4 
0 
u https://doi.org/10.1007/9781461334378?nosfx=y
x Verlag
3 Volltext

082 
0 

a 003.3

520 


a Optimization problems abound in most fields of science, engineering, and tech nology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NPhard prob lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solving large classes of problems from diverse areas such as engineering design and control, computational chemistry and biology, structural optimization, computer science, operations research, and economics. This book contains refereed invited papers presented at the conference on "State of the Art in Global Optimization: Computational Methods and Applications" held at Princeton University, April 2830, 1995. The conference presented current re search on global optimization and related applications in science and engineering. The papers included in this book cover a wide spectrum of approaches for solving global optimization problems and applications
