scholarly journals An interior point type QP-free algorithm with superlinear convergence for inequality constrained optimization

2007 ◽  
Vol 31 (6) ◽  
pp. 1201-1212 ◽  
Author(s):  
Zhibin Zhu
Keyword(s):  
Constrained Optimization ◽  
Interior Point ◽  
Superlinear Convergence ◽  
Inequality Constrained Optimization ◽  
Point Type

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.

scholarly journals A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Zhijun Luo ◽  
Lirong Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Descent Direction ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Globally Convergent ◽  
Variable Distribution ◽  
Systems Of Linear Equations

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

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