This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date
Large-Scale Nonlinear Optimization
β Scribed by Gianni Pillo, Massimo Roma
- Publisher
- Springer
- Year
- 2006
- Tongue
- English
- Leaves
- 296
- Series
- Nonconvex Optimization and Its Applications
- Edition
- 1st Edition.
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.
π SIMILAR VOLUMES
<p><P><STRONG>Large-Scale Nonlinear Optimization </STRONG>reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.</P><P></P><P>The chapters of t
<p><span>Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.</span></p><p><span>The chapters of the book,
<p>LANCELOT is a software package for solving large-scale nonlinear optimization problems. This book is our attempt to provide a coherent overview of the package and its use. This includes details of how one might present examples to the package, how the algorithm tries to solve these examples and v
<p><P>Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary opti
<p>When analyzing systems with a large number of parameters, the dimenΒ sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other