A Neural Network Model for Non-smooth Optimization over a Compact Convex Subset

2006 ◽  
pp. 344-349 ◽  
Author(s):  
Guocheng Li ◽  
Shiji Song ◽  
Cheng Wu ◽  
Zifang Du
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Convex Subset ◽  
Compact Convex ◽  
Compact Convex Subset ◽  
Smooth Optimization

A Novel Collaborate Neural Dynamic System Model for Solving a Class of Min–Max Optimization Problems with an Application in Portfolio Management

2018 ◽  
Vol 62 (7) ◽  
pp. 1061-1085
Author(s):  
Alireza Nazemi ◽  
Marziyeh Mortezaee
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Portfolio Management ◽  
Original Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Convex Programming Problem ◽  
Neural Dynamic ◽  
Smooth Optimization

Abstract In this paper, we describe a new neural network model for solving a class of non-smooth optimization problems with min–max objective function. The basic idea is to replace the min–max function by a smooth one using an entropy function. With this smoothing technique, the non-smooth problem is converted into an equivalent differentiable convex programming problem. A neural network model is then constructed based on Karush–Kuhn–Tucker optimality conditions. It is investigated that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. As an application in economics, we use the proposed scheme to a min–max portfolio optimization problems. The effectiveness of the method is demonstrated by several numerical simulations.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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