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.

3-1 Piecewise NCP Function for New Nonmonotone QP-Free Infeasible Method

2014 ◽  
Vol 26 (5) ◽  
pp. 566-572 ◽  
Author(s):  
Ailan Liu ◽  
◽  
Dingguo Pu ◽  
◽  
Keyword(s):  
Constraint Qualification ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Ncp Function ◽  
Line Searches ◽  
Complementarity Condition ◽  
Linear Independence Constraint Qualification ◽  
Systems Of Linear Equations

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/04.jpg"" width=""300"" />Algorithm flow chart</div> We propose a nonmonotone QP-free infeasible method for inequality-constrained nonlinear optimization problems based on a 3-1 piecewise linear NCP function. This nonmonotone QP-free infeasible method is iterative and is based on nonsmooth reformulation of KKT first-order optimality conditions. It does not use a penalty function or a filter in nonmonotone line searches. This algorithm solves only two systems of linear equations with the same nonsingular coefficient matrix, and is implementable and globally convergent without a linear independence constraint qualification or a strict complementarity condition. Preliminary numerical results are presented. </span>

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.

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.

SEBSM-Based Iterative Method for Solving Large Systems of Linear Equations and Its Applications in Engineering Computation

2016 ◽  
Vol 13 (05) ◽  
pp. 1650024 ◽  
Author(s):  
Jin-Xiu Hu ◽  
Xiao-Wei Gao ◽  
Zhi-Chao Yuan ◽  
Jian Liu ◽  
Shi-Zhang Huang
Keyword(s):  
Iterative Method ◽  
Direct Method ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Relaxation Factor ◽  
Banded Matrix ◽  
Speed Up ◽  
Systems Of Linear Equations ◽  
Matrix Formula ◽  
Large Systems

In this paper, a new iterative method, for solving sparse nonsymmetrical systems of linear equations is proposed based on the Simultaneous Elimination and Back-Substitution Method (SEBSM), and the method is applied to solve systems resulted in engineering problems solved using Finite Element Method (FEM). First, SEBSM is introduced for solving general linear systems using the direct method. And, then an iterative method based on SEBSM is presented. In the method, the coefficient matrix [Formula: see text] is split into lower, diagonally banded and upper matrices. The iterative convergence can be controlled by selecting a suitable bandwidth of the diagonally banded matrix. And the size of the working array needing to be stored in iteration is as small as the bandwidth of the diagonally banded matrix. Finally, an accelerating strategy for this iterative method is proposed by introducing a relaxation factor, which can speed up the convergence effectively if an optimal relaxation factor is chosen. Two numerical examples are given to demonstrate the behavior of the proposed method.

ACCUMULATION OF THE CHOLESKY SQUARE ROOT IN HELMERT BLOCKING

1986 ◽  
Vol 40 (3) ◽  
pp. 297-314
Author(s):  
D. R. Junkins ◽  
R. R. Steeves
Keyword(s):  
Efficient Solution ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Computational Method ◽  
Normal Equation ◽  
Square Root ◽  
Unknown Parameters ◽  
Normal Equations ◽  
Systems Of Linear Equations ◽  
Large Systems

The Helmert blocking method is being used in the present effort to readjust North American geodetic networks. Combining this method with the Cholesky computational method enables the efficient solution of very large systems of linear equations. A by-product of this approach is a “partial” Cholesky square root for each Helmert block. This paper demonstrates that the Cholesky square root for the entire system of normal equations can be constructed from partial Cholesky square root blocks that are produced during the Helmert block adjustment, even though various reorderings of the unknown parameters are necessary throughout the computations. The entire Cholesky square root can be used to compute the inverse of the normal equation coefficient matrix, which is needed for post-adjustment statistical analyses.

scholarly journals A QP-Free Algorithm for Finite Minimax Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Daolan Han ◽  
Jinbao Jian ◽  
Qinfeng Zhang
Keyword(s):  
Minimax Problems ◽  
Linear Equations ◽  
Inequality Constraints ◽  
Coefficient Matrix ◽  
Systems Of Linear Equations ◽  
Full Dimension ◽  
Finite Minimax ◽  
The Stability ◽  
Nonlinear Minimax Problems ◽  
Nonlinear Minimax

The nonlinear minimax problems without constraints are discussed. Due to the expensive computation for solving QP subproblems with inequality constraints of SQP algorithms, in this paper, a QP-free algorithm which is also called sequential systems of linear equations algorithm is presented. At each iteration, only two systems of linear equations with the same coefficient matrix need to be solved, and the dimension of each subproblem is not of full dimension. The proposed algorithm does not need any penalty parameters and barrier parameters, and it has small computation cost. In addition, the parameters in the proposed algorithm are few, and the stability of the algorithm is well. Convergence property is described and some numerical results are provided.

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