A Line Search Exact Penalty Method for Nonlinear Semidefinite Programming

2019 ◽  
Vol 08 (04) ◽  
pp. 638-649
Author(s):  
加其 吴
2010 ◽  
Vol 133 (1-2) ◽  
pp. 39-73 ◽  
Author(s):  
Richard H. Byrd ◽  
Gabriel Lopez-Calva ◽  
Jorge Nocedal

2015 ◽  
Vol 32 (01) ◽  
pp. 1540006 ◽  
Author(s):  
Zhongwen Chen ◽  
Shicai Miao

In this paper, we propose a class of new penalty-free method, which does not use any penalty function or a filter, to solve nonlinear semidefinite programming (NSDP). So the choice of the penalty parameter and the storage of filter set are avoided. The new method adopts trust region framework to compute a trial step. The trial step is then either accepted or rejected based on the some acceptable criteria which depends on reductions attained in the nonlinear objective function and in the measure of constraint infeasibility. Under the suitable assumptions, we prove that the algorithm is well defined and globally convergent. Finally, the preliminary numerical results are reported.


2019 ◽  
Vol 53 (1) ◽  
pp. 29-38
Author(s):  
Larbi Bachir Cherif ◽  
Bachir Merikhi

This paper presents a variant of logarithmic penalty methods for nonlinear convex programming. If the descent direction is obtained through a classical Newton-type method, the line search is done on a majorant function. Numerical tests show the efficiency of this approach versus classical line searches.


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