Algorithms for Unconstrained Nonlinear Programming

  • Xiang-Sun Zhang
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 46)


A library of algorithms, for solving unconstrained nonlinear programming problems
$$\mathop {\min }\limits_{x \in {R^n}} f\left( x \right),$$
has been presented in the mathematical programming literatures. There are many excellent books dealing with the algorithms comprehensively (to mention a few, Avriel [17], Bazaraa and Shetty [27], Fletcher [105], Gill, Murray and Wright [123], and Luenberger [205]). In this section we only introduce some basic algorithms which have already been frequently used or would be used in the future in artificial neural network study. The same consideration will be taken when we arrange the materials for the other chapters.


Newton Method Line Search Hessian Matrix Conjugate Gradient Method Descent Direction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Xiang-Sun Zhang
    • 1
  1. 1.Academy of Mathematics and Systems, Institute of Applied MathematicsChinese Academy of SciencesChina

Personalised recommendations