scholarly journals A simple feasible SQP method for inequality constrained optimization with global and superlinear convergence

2010 ◽  
Vol 233 (11) ◽  
pp. 3060-3073 ◽  
Author(s):  
Zhong Jin ◽  
Yuqing Wang
Keyword(s):  
Constrained Optimization ◽  
Superlinear Convergence ◽  
Sqp Method ◽  
Inequality Constrained Optimization ◽  
Global And Superlinear Convergence

scholarly journals A Globally Convergent Line Search Filter SQP Method for Inequality Constrained Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhong Jin
Keyword(s):  
Constrained Optimization ◽  
Line Search ◽  
Convergence Property ◽  
Step Size ◽  
Sqp Method ◽  
Search Filter ◽  
Inequality Constrained Optimization ◽  
Backtracking Line Search ◽  
Global Convergence Property ◽  
Determine Step

A line search filter SQP method for inequality constrained optimization is presented. This method makes use of a backtracking line search procedure to generate step size and the efficiency of the filter technique to determine step acceptance. At each iteration, the subproblem is always consistent, and it only needs to solve one QP subproblem. Under some mild conditions, the global convergence property can be guaranteed. In the end, numerical experiments show that the method in this paper is effective.

scholarly journals A Generalized Gradient Projection Filter Algorithm for Inequality Constrained Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Wang ◽  
Shaoli Hua ◽  
Junjie Tang
Keyword(s):  
Global Convergence ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Superlinear Convergence ◽  
Gradient Projection ◽  
Generalized Gradient ◽  
Inequality Constrained Optimization ◽  
Mild Conditions ◽  
The Third ◽  
Constrained Programming

A generalized gradient projection filter algorithm for inequality constrained optimization is presented. It has three merits. The first is that the amount of computation is lower, since the gradient matrix only needs to be computed one time at each iterate. The second is that the paper uses the filter technique instead of any penalty function for constrained programming. The third is that the algorithm is of global convergence and locally superlinear convergence under some mild conditions.

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