<span>Many questions dealing with solvability, stability and solution methods for va- ational inequalities or equilibrium, optimization and complementarity problems lead to the analysis of certain (perturbed) equations. This often requires a - formulation of the initial model being under considerati
Multiscale Optimization Methods and Applications (Nonconvex Optimization and Its Applications, 82)
β Scribed by William W. Hager (editor), Shu-Jen Huang (editor), Panos M. Pardalos (editor), Oleg A. Prokopyev (editor)
- Publisher
- Springer
- Year
- 2005
- Tongue
- English
- Leaves
- 416
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research. The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.
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Dedicated to the 70th birthday of Professor J. Mockus, whose scientific interpretive theory and applications of global and discrete optimization, and stochastic programming were selected due to the theoretical soundness combined with practical applicability.
As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards th
<p><P>As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towa
<p><P>As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towa