A class of trust region methods for linear inequality constrained optimization and its theory analysis II. local convergence rate and numerical tests

1995 ◽  
Vol 10 (4) ◽  
pp. 439-448
Author(s):  
Naihua Xiu
Keyword(s):  
Convergence Rate ◽  
Constrained Optimization ◽  
Local Convergence ◽  
Linear Inequality ◽  
Trust Region ◽  
Trust Region Methods ◽  
Theory Analysis ◽  
Inequality Constrained Optimization ◽  
Numerical Tests ◽  
Linear Inequality Constrained Optimization

scholarly journals A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhensheng Yu ◽  
Jinhong Yu
Keyword(s):  
Global Convergence ◽  
Penalty Function ◽  
Optimization Problems ◽  
Trust Region ◽  
Trust Region Methods ◽  
Constrained Optimization Problems ◽  
Trust Region Algorithm ◽  
Numerical Tests ◽  
Equality Constrained Optimization ◽  
Nonlinear Equality

We present a nonmonotone trust region algorithm for nonlinear equality constrained optimization problems. In our algorithm, we use the average of the successive penalty function values to rectify the ratio of predicted reduction and the actual reduction. Compared with the existing nonmonotone trust region methods, our method is independent of the nonmonotone parameter. We establish the global convergence of the proposed algorithm and give the numerical tests to show the efficiency of the algorithm.

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