A new sequential systems of linear equations algorithm of feasible descent for inequality constrained optimization

2010 ◽  
Vol 26 (12) ◽  
pp. 2399-2420 ◽  
Author(s):  
Jin Bao Jian ◽  
Dao Lan Han ◽  
Qing Juan Xu
Keyword(s):  
Constrained Optimization ◽  
Linear Equations ◽  
Inequality Constrained Optimization ◽  
Sequential Systems ◽  
Systems Of Linear Equations

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.

AN INFEASIBLE SSLE FILTER ALGORITHM FOR GENERAL CONSTRAINED OPTIMIZATION WITHOUT STRICT COMPLEMENTARITY

2011 ◽  
Vol 28 (03) ◽  
pp. 361-399 ◽  
Author(s):  
CHUNGEN SHEN ◽  
WENJUAN XUE ◽  
DINGGUO PU
Keyword(s):  
Linear Independence ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Active Set ◽  
Strict Complementarity ◽  
Complementarity Condition ◽  
Sequential Systems ◽  
Computational Effect ◽  
Systems Of Linear Equations ◽  
One Step

In this paper, we propose a new sequential systems of linear equations (SSLE) filter algorithm, which is an infeasible QP-free method. The new algorithm needs to solve a few reduced systems of linear equations with the same nonsingular coefficient matrix, and after finitely many iterations, only two linear systems need to be solved. Furthermore, the nearly active set technique is used to improve the computational effect. Under the linear independence condition, the global convergence is proved. In particular, the rate of convergence is proved to be one-step superlinear without assuming the strict complementarity condition. Numerical results and comparison with other algorithms indicate that the new algorithm is promising.

A Globally and Superlinearly Convergent Primal-dual Interior Point Method for General Constrained Optimization

2015 ◽  
Vol 8 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Jianling Li ◽  
Jian Lv ◽  
Jinbao Jian
Keyword(s):  
Constrained Optimization ◽  
Interior Point ◽  
Interior Point Method ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Point Method ◽  
Active Set ◽  
Primal Dual ◽  
Systems Of Linear Equations ◽  
Working Set

AbstractIn this paper, a primal-dual interior point method is proposed for general constrained optimization, which incorporated a penalty function and a kind of new identification technique of the active set. At each iteration, the proposed algorithm only needs to solve two or three reduced systems of linear equations with the same coefficient matrix. The size of systems of linear equations can be decreased due to the introduction of the working set, which is an estimate of the active set. The penalty parameter is automatically updated and the uniformly positive definiteness condition on the Hessian approximation of the Lagrangian is relaxed. The proposed algorithm possesses global and superlinear convergence under some mild conditions. Finally, some preliminary numerical results are reported.

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