Exact Penalty in Constrained Optimization and the Mordukhovich Basic Subdifferential

2010 ◽  
pp. 223-232 ◽  
Author(s):  
Alexander J. Zaslavski
Keyword(s):  
Constrained Optimization ◽  
Exact Penalty

Sufficient conditions for exact penalty in constrained optimization

PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 2060025-2060026
Author(s):  
Alexander J. Zaslavski
Keyword(s):  
Constrained Optimization ◽  
Sufficient Conditions ◽  
Exact Penalty

Distributed constrained optimization protocol via an exact penalty method

2015 ◽  
Author(s):  
Izumi Masubuchi ◽  
Takayuki Wada ◽  
Toru Asai ◽  
Nguyen Thi Hoai Linh ◽  
Yuzo Ohta ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Penalty Method ◽  
Exact Penalty

scholarly journals A family of global convergent inexact secant methods for nonconvex constrained optimization

2018 ◽  
Vol 12 (2) ◽  
pp. 165-176
Author(s):  
Zhujun Wang ◽  
Li Cai ◽  
Zheng Peng
Keyword(s):  
Global Convergence ◽  
Constrained Optimization ◽  
Line Search ◽  
Merit Function ◽  
Exact Penalty ◽  
Search Technique ◽  
Nonconvex Constrained Optimization ◽  
Secant Methods ◽  
Armijo Line Search ◽  
Line Search Technique

We present a family of new inexact secant methods in association with Armijo line search technique for solving nonconvex constrained optimization. Different from the existing inexact secant methods, the algorithms proposed in this paper need not compute exact directions. By adopting the nonsmooth exact penalty function as the merit function, the global convergence of the proposed algorithms is established under some reasonable conditions. Some numerical results indicate that the proposed algorithms are both feasible and effective.

scholarly journals Smoothing Approximation to the Square-Order Exact Penalty Functions for Constrained Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shujun Lian ◽  
Jinli Han
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Original Problem ◽  
Optimal Solution ◽  
Penalty Functions ◽  
Exact Penalty Functions ◽  
Exact Penalty ◽  
Inequality Constrained Optimization ◽  
Numerical Examples ◽  
Mild Conditions

A method is proposed to smooth the square-order exact penalty function for inequality constrained optimization. It is shown that, under some conditions, an approximately optimal solution of the original problem can be obtained by searching an approximately optimal solution of the smoothed penalty problem. An algorithm based on the smoothed penalty functions is given. The algorithm is shown to be convergent under mild conditions. Two numerical examples show that the algorithm seems efficient.

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