A Line Search Exact Penalty Method for Nonlinear Semidefinite Programming

2019 ◽  
Vol 08 (04) ◽  
pp. 638-649
Author(s):  
加其 吴
Keyword(s):  
Semidefinite Programming ◽  
Penalty Method ◽  
Line Search ◽  
Exact Penalty ◽  
Nonlinear Semidefinite Programming

scholarly journals A line search exact penalty method using steering rules

2010 ◽  
Vol 133 (1-2) ◽  
pp. 39-73 ◽  
Author(s):  
Richard H. Byrd ◽  
Gabriel Lopez-Calva ◽  
Jorge Nocedal
Keyword(s):  
Penalty Method ◽  
Line Search ◽  
Exact Penalty

A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming

2015 ◽  
Vol 32 (01) ◽  
pp. 1540006 ◽  
Author(s):  
Zhongwen Chen ◽  
Shicai Miao
Keyword(s):  
Semidefinite Programming ◽  
Objective Function ◽  
Penalty Function ◽  
Trust Region ◽  
Numerical Results ◽  
New Method ◽  
Penalty Parameter ◽  
Nonlinear Semidefinite Programming ◽  
Globally Convergent ◽  
Nonlinear Objective Function

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.

A penalty method for nonlinear programming

2019 ◽  
Vol 53 (1) ◽  
pp. 29-38
Author(s):  
Larbi Bachir Cherif ◽  
Bachir Merikhi
Keyword(s):  
Nonlinear Programming ◽  
Convex Programming ◽  
Penalty Method ◽  
Line Search ◽  
Penalty Methods ◽  
Descent Direction ◽  
Type Method ◽  
Numerical Tests ◽  
Line Searches ◽  
Majorant Function

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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