Paper available: Transformation of NLP Problems Using Neural Nets

[Bao-Liang Lu]

The following paper, to appear in Annals of Mathematics and Artificial 
Intelligence, is available via anonymous FTP.

(This work was presented at 1st Mathematics of Neural Networks and 
Applications (MANNA'95) Conference, 3-7 July 1995, Lady Margaret Hall,
Oxford, UK) 

FTP-host:ftp.bmc.riken.go.jp

FTP-file:pub/publish/Lu/lu-manna95.ps.Z

==========================================================================
TITLE: Transformation of Nonlinear Programming Problems into Separable
       Ones Using Multilayer Neural Networks
       
AUTHORS:
       Bao-Liang Lu (1)
       Koji Ito (1,2)

ORGANISATIONS:
       (1) The Institute of Physical and Chemical Research (RIKEN)
       (2) Toyohashi University of Technology
 
ABSTRACT:
In this paper we present a novel method for transforming nonseparable 
nonlinear programming (NLP) problems into separable ones using multilayer 
neural networks. This method is based on a useful feature of multilayer 
neural networks, i.e., any nonseparable function can be approximately 
expressed as a separable one by a multilayer neural network. By use of 
this method, the nonseparable objective and (or) constraint functions in 
NLP problems can be approximated by multilayer neural networks, 
and therefore, any nonseparable NLP problem can be transformed into 
a separable one. The importance of this method lies in the fact that it 
provides us with a promising approach to using modified simplex methods
to solve general NLP problems.

(6 pages. No hard copies available.)

Bao-Liang Lu
---------------------------------------------
Bio-Mimetic Control Research Center,
The Institute of Physical and Chemical Research (RIKEN)
3-8-31 Rokuban, Atsuta-ku, Nagoya 456, Japan
Phone: +81-52-654-9137
Fax: +81-52-654-9138
Email: lbl at nagoya.bmc.riken.go.jp