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.

scholarly journals An SQP trust region method for solving the discrete-time linear quadratic control problem

2012 ◽  
Vol 22 (2) ◽  
pp. 353-363 ◽  
Author(s):  
El-Sayed Mostafa
Keyword(s):  
Control Problem ◽  
Discrete Time ◽  
Optimization Problem ◽  
Trust Region Method ◽  
Trust Region ◽  
Linear Quadratic Control ◽  
Linear Quadratic ◽  
Region Method ◽  
Time Linear ◽  
Linear Quadratic Control Problem

An SQP trust region method for solving the discrete-time linear quadratic control problemIn this paper, a sequential quadratic programming method combined with a trust region globalization strategy is analyzed and studied for solving a certain nonlinear constrained optimization problem with matrix variables. The optimization problem is derived from the infinite-horizon linear quadratic control problem for discrete-time systems when a complete set of state variables is not available. Moreover, a parametrization approach is introduced that does not require starting a feasible solution to initiate the proposed SQP trust region method. To demonstrate the effectiveness of the method, some numerical results are presented in detail.

scholarly journals A Stochastic Trust Region Method for Unconstrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Ningning Li ◽  
Dan Xue ◽  
Wenyu Sun ◽  
Jing Wang
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Unconstrained Minimization ◽  
Quadratic Model ◽  
Convergence Properties ◽  
Region Method ◽  
Unconstrained Optimization Problems

In this paper, a stochastic trust region method is proposed to solve unconstrained minimization problems with stochastic objectives. Particularly, this method can be used to deal with nonconvex problems. At each iteration, we construct a quadratic model of the objective function. In the model, stochastic gradient is used to take the place of deterministic gradient for both the determination of descent directions and the approximation of the Hessians of the objective function. The behavior and the convergence properties of the proposed method are discussed under some reasonable conditions. Some preliminary numerical results show that our method is potentially efficient.

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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