A new method for solving trajectory fusion estimation model based on trust region

2015 ◽  
pp. 83-86 ◽  
Keyword(s):  
Trust Region ◽  
New Method ◽  
Estimation Model ◽  
Model Based

A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming

2015 ◽  
Vol 32 (01) ◽  
pp. 1540006 ◽  
Author(s):  
Zhongwen Chen ◽  
Shicai Miao
Keyword(s):  
Semidefinite Programming ◽  
Objective Function ◽  
Penalty Function ◽  
Trust Region ◽  
Numerical Results ◽  
New Method ◽  
Penalty Parameter ◽  
Nonlinear Semidefinite Programming ◽  
Globally Convergent ◽  
Nonlinear Objective Function

In this paper, we propose a class of new penalty-free method, which does not use any penalty function or a filter, to solve nonlinear semidefinite programming (NSDP). So the choice of the penalty parameter and the storage of filter set are avoided. The new method adopts trust region framework to compute a trial step. The trial step is then either accepted or rejected based on the some acceptable criteria which depends on reductions attained in the nonlinear objective function and in the measure of constraint infeasibility. Under the suitable assumptions, we prove that the algorithm is well defined and globally convergent. Finally, the preliminary numerical results are reported.

Dynamic Validation and Sensitivity Analysis of a NOx Estimation Model Based on In-Cylinder Pressure Measurement

2017 ◽  
Author(s):  
Sergio Mario Camporeale ◽  
Patrizia D. Ciliberti ◽  
Antonio Carlucci ◽  
Daniela Ingrosso
Keyword(s):  
Sensitivity Analysis ◽  
Pressure Measurement ◽  
Cylinder Pressure ◽  
Estimation Model ◽  
Model Based

scholarly journals A Simple Alternating Direction Method for the Conic Trust Region Subproblem

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Honglan Zhu ◽  
Qin Ni
Keyword(s):  
Trust Region ◽  
Alternating Direction Method ◽  
New Method ◽  
Quadratic Model ◽  
Descent Direction ◽  
Orthogonal Direction ◽  
Trust Region Subproblem ◽  
One Dimensional ◽  
Alternating Direction ◽  
Low Dimensional

A simple alternating direction method is used to solve the conic trust region subproblem of unconstrained optimization. By use of the new method, the subproblem is solved by two steps in a descent direction and its orthogonal direction, the original conic trust domain subproblem into a one-dimensional subproblem and a low-dimensional quadratic model subproblem, both of which are very easy to solve. Then the global convergence of the method under some reasonable conditions is established. Numerical experiment shows that the new method seems simple and effective.

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