𝔖 Scriptorium
✦   LIBER   ✦
📁

Conjugate Gradient Algorithms in Nonconvex Optimization

✍ Scribed by Radosław Pytlak (auth.)

Publisher
Springer-Verlag Berlin Heidelberg
Year
2009
Tongue
English
Leaves
492
Series
Nonconvex Optimization and Its Applications 89
Edition
1
Category
Library
⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Subjects

Calculus of Variations and Optimal Control; Optimization; Operations Research/Decision Theory; Quality Control, Reliability, Safety and Risk

📜 SIMILAR VOLUMES

Conjugate Gradient Algorithms in Nonconv
📁 Conjugate Gradient Algorithms in Nonconvex Optimization
✍ Radosław Pytlak (auth.) 📂 Library 📅 2009 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p><P>This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. </P><P>Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular mem

Global Optimization in Action: Continuou
📁 Global Optimization in Action: Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications (Nonconvex Optimization and Its Applications)
✍ Janos D. Pinter 📂 Library 📅 1995 🌐 English

In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints. In many cases of practical relevance, the optimization problem structure does not warrant the global optimality of loca

Conjugate Gradient Algorithms and Finite
📁 Conjugate Gradient Algorithms and Finite Element Methods
✍ Alena à olcová (auth.), Prof. Michal Křížek, Prof. Pekka Neittaanmäki, Dr. Serge 📂 Library 📅 2004 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p><P> The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well. Via their combinations practitioners have been able to solve differential equations and multidimensional problems modele

Nonlinear Conjugate Gradient Methods for
📁 Nonlinear Conjugate Gradient Methods for Unconstrained Optimization
✍ Neculai Andrei 📂 Library 📅 2020 🏛 Springer 🌐 English

<p></p><p>Two approaches are known for solving <i>large-scale</i> unconstrained optimization problems―the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and converge