A Feasible SQP Method Using Augmented Lagrangian Function for General Constrained Optimization

2012 ◽  
Author(s):  
Xiaowei Jiang ◽  
Yueting Yang ◽  
Yunlong Lu
Keyword(s):  
Constrained Optimization ◽  
Augmented Lagrangian ◽  
Lagrangian Function ◽  
Augmented Lagrangian Function ◽  
Sqp Method

scholarly journals A New Augmented Lagrangian Method for Equality Constrained Optimization with Simple Unconstrained Subproblem

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Hao Zhang ◽  
Qin Ni
Keyword(s):  
Constrained Optimization ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Lagrangian Method ◽  
Lagrangian Function ◽  
New Method ◽  
Quadratic Model ◽  
Quadratic Term ◽  
Augmented Lagrangian Function ◽  
Equality Constrained Optimization

We propose a new method for equality constrained optimization based on augmented Lagrangian method. We construct an unconstrained subproblem by adding an adaptive quadratic term to the quadratic model of augmented Lagrangian function. In each iteration, we solve this unconstrained subproblem to obtain the trial step. The main feature of this work is that the subproblem can be more easily solved. Numerical results show that this method is effective.

Local Saddle Points Theory of a New Augmented Lagrangians

2010 ◽  
Vol 121-122 ◽  
pp. 123-127
Author(s):  
Wen Ling Zhao ◽  
Jing Zhang ◽  
Jin Chuan Zhou
Keyword(s):  
Saddle Point ◽  
Augmented Lagrangian ◽  
Saddle Points ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Lagrangian Function ◽  
Augmented Lagrangians ◽  
Augmented Lagrangian Function ◽  
Primal Problem ◽  
Local Optimal Solution

In connection with Problem (P) with both the equality constraints and inequality constraints, we introduce a new augmented lagrangian function. We establish the existence of local saddle point under the weaker sufficient second order condition, discuss the relationships between local optimal solution of the primal problem and local saddle point of the augmented lagrangian function.

A Kind of NLP Algorithm with NCP Function

2011 ◽  
Vol 467-469 ◽  
pp. 877-881
Author(s):  
Ai Ping Jiang ◽  
Feng Wen Huang
Keyword(s):  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Merit Function ◽  
Lagrangian Function ◽  
Filter Method ◽  
Augmented Lagrangian Function ◽  
Penalty Term ◽  
Ncp Function ◽  
Modified Algorithm ◽  
Constrained Function

In this paper, two modifications are proposed for minimizing the nonlinear optimization problem (NLP) based on Fletcher and Leyffer’s filter method which is different from traditional merit function with penalty term. We firstly modify one component of filter pairs with NCP function instead of violation constrained function in order to avoid the difficulty of selecting penalty parameters. We also proved that the modified algorithm is globally and super linearly convergent under certain conditions. We secondly convert objective function to augmented Lagrangian function in case of incompatibility caused by sub-problems.

Sign in / Sign up

Export Citation Format

Share Document