Constrained Variable Metric Method With Feasible Directions

Author(s):  
Wang Jianhua ◽  
Zhou Ji ◽  
Yu Jun

Abstract This paper proposes a new feasible direction algorithm based on the constrained variable metric method of Powell in order to handle the design optimization problmes which demand that all iterative points are feasible. The algorithm retains many advantages of the constrained variable metric method, makes use of the properties of the solutions of quadratic programming problems and information of iterative points to define feasible directions, and uses the monotonicity analysis to establish the linesearch strategy which is especially suitable for feasible direction algorithms and a simple and efficient method for finding feasible initial points. The numerical results presented in the paper demonstrate that its rate of convergence is faster than those of Powell’s method and another feasible direction algorithm of Herskovits and its iterative procedure avoids Maratos effect.

1984 ◽  
Vol 106 (1) ◽  
pp. 90-94 ◽  
Author(s):  
J. Zhou ◽  
R. W. Mayne

The concept of monotonicity analysis is combined with the reduced gradient technique for design optimization. The combination is based on a strategy for applying monotonicity analysis in specified search directions and incorporates this strategy into an iterative procedure for identifying the active inequality constraints in an optimization problem. As the active constraints are identified, the reduced gradient information based on these constraints forms the basis for a variable metric search leading to the constrained optimum. Test results are presented which indicate the performance of two different algorithms using this approach.


2018 ◽  
Vol 34 (6) ◽  
pp. 1322-1341 ◽  
Author(s):  
Alfredo Canelas ◽  
Miguel Carrasco ◽  
Julio López

Author(s):  
Abdelkrim El Mouatasim ◽  
Rachid Ellaia ◽  
Eduardo de Cursi

Random perturbation of the projected variable metric method for nonsmooth nonconvex optimization problems with linear constraintsWe present a random perturbation of the projected variable metric method for solving linearly constrained nonsmooth (i.e., nondifferentiable) nonconvex optimization problems, and we establish the convergence to a global minimum for a locally Lipschitz continuous objective function which may be nondifferentiable on a countable set of points. Numerical results show the effectiveness of the proposed approach.


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