Simultaneous Inversion of Hypocenters and Velocity Structure using the Quasi-Newton Method and Trust-Region Method

2006 ◽  
Vol 49 (3) ◽  
pp. 740-750 ◽  
Author(s):  
Yue TIAN ◽  
Xiao-Fei CHEN
Keyword(s):  
Newton Method ◽  
Velocity Structure ◽  
Trust Region Method ◽  
Trust Region ◽  
Simultaneous Inversion ◽  
Region Method ◽  
Quasi Newton

scholarly journals A quasi-Newton trust region method based on a new fractional model

2015 ◽  
Vol 5 (3) ◽  
pp. 237-249 ◽  
Author(s):  
Honglan Zhu ◽  
◽  
Qin Ni ◽  
Meilan Zeng ◽  
Keyword(s):  
Trust Region Method ◽  
Trust Region ◽  
Fractional Model ◽  
Region Method ◽  
Quasi Newton

Computation of Common Normal Between Wheelset and Rail as an Optimization Problem

2015 ◽  
Author(s):  
Ashok Singamaneni ◽  
Sunil K. Ballamudi ◽  
Behrooz Fallahi
Keyword(s):  
Newton Method ◽  
Optimization Problem ◽  
Trust Region Method ◽  
Trust Region ◽  
Numerical Results ◽  
Optimization Techniques ◽  
Region Method ◽  
The Common

Computation of Common Normal between a wheelset and rail is a key problem in simulation of wheel/rail interaction. Such an algorithm must be robust and efficient. In this work, computation of location of the common normal is formulated as an optimization problem. This framework has the advantage of being able to use optimization techniques for determination of location of common normal as an alternative Newton method. The numerical results obtained using Trust-Region Method are presented.

Based on GA Mixed with Trust Region Method Solving Nonlinear Least Square Problems

2011 ◽  
Vol 141 ◽  
pp. 92-97
Author(s):  
Miao Hu ◽  
Tai Yong Wang ◽  
Bo Geng ◽  
Qi Chen Wang ◽  
Dian Peng Li
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Least Square ◽  
Search Range ◽  
Genetic Search ◽  
Region Method ◽  
Unconstrained Optimization Problems ◽  
Least Square Problems

Nonlinear least square is one of the unconstrained optimization problems. In order to solve the least square trust region sub-problem, a genetic algorithm (GA) of global convergence was applied, and the premature convergence of genetic algorithms was also overcome through optimizing the search range of GA with trust region method (TRM), and the convergence rate of genetic algorithm was increased by the randomness of the genetic search. Finally, an example of banana function was established to verify the GA, and the results show the practicability and precision of this algorithm.

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