Results 131 to 140 of about 13,831 (172)
On the exactness of a class of nondifferentiable penalty functions [PDF]
Journal of Optimization Theory and Applications, 1988 We consider a class of non-differentiable penalty functions for the solution of nonlinear programming problems without convexity assumptions. Preliminarily, we introduce a notion of exactness which appears to be of relevance in connection with the solution of the constrained problem by means of unconstrained minimization methods. Then, we show that the
DI PILLO, Gianni, GRIPPO, Luigi
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Characterizations for perturbed exact penalty functions [PDF]
Nonlinear Analysis: Theory, Methods & Applications, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, X. Q., Ralph, D.
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On Smoothing Exact Penalty Functions for Convex Constrained Optimization
SIAM Journal on Optimization, 1994Summary: A quadratic smoothing approximation to nondifferentiable exact penalty functions for convex constrained optimization is proposed and its properties are established. The smoothing approximation is used as the basis of an algorithm for solving problems with (i) embedded network structures, and (ii) nonlinear minimax problems. Extensive numerical
Mustafa C Pinar, Stavros A Zenios
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Exact penalty functions and Lagrange multipliers
Optimization, 1991In this paper we consider a class of nondifferentiable penalty functions associated with a Lipschitz programming problem with an abstract geometric constraint. We analyse the relationship between this class of functions and Kuhn-Tucker type necessary conditions for the programming problem.
FRANCISCO Facchinei
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A lower bound for the controlling parameters of the exact penalty functions
Mathematical Programming, 1978The purpose of this paper is to present new exact penalty functions and discuss their properties. A lower bound on the controlling parameters is given, for which above this value, the optimum of the exact penalty function coincides with the optimum of the nonlinear programming problem.
Christakis Charalambous
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Exact penalty functions and stability in locally Lipschitz programming
Mathematical Programming, 1984We extend the theory of exact penalty functions for nonlinear programs whose objective functions and equality and inequality constraints are locally Lipschitz; arbitrary simple constraints are also allowed. Assuming a weak stability condition, we show that for all sufficiently large penalty parameter values an isolated minimum of the nonlinear program ...
Eric Rosenberg, Rosenberg Eric
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Journal of Global Optimization, 2019
For nonlinear optimization problems with equality, inequality and bound constraints, the authors consider a family of penalty functions and prove that, under suitable assumptions, which include a so-called weakly generalized Mangasarian-Fromovitz constraint qualification, when the penalty parameter is large enough every local optimal solution ...
Yang Zhou
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For nonlinear optimization problems with equality, inequality and bound constraints, the authors consider a family of penalty functions and prove that, under suitable assumptions, which include a so-called weakly generalized Mangasarian-Fromovitz constraint qualification, when the penalty parameter is large enough every local optimal solution ...
Yang Zhou
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SIAM Journal on Optimization, 2003
A new approach to exact penalization of a constrained, nonlinear optimization problem is introduced. This is motivated by the desire to deal with the following list of perceived failures of other exact penalty methods: 1. nonsmoothness is avoided; 2. the penalized objective remains bounded below under mild assumptions; 3.
Waltraud Huyer, Arnold Neumaier
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A new approach to exact penalization of a constrained, nonlinear optimization problem is introduced. This is motivated by the desire to deal with the following list of perceived failures of other exact penalty methods: 1. nonsmoothness is avoided; 2. the penalized objective remains bounded below under mild assumptions; 3.
Waltraud Huyer, Arnold Neumaier
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A dual differentiable exact penalty function
Mathematical Programming, 1983A new penalty function is associated with an inequality constrained nonlinear programming problem via its dual. This penalty function is globally differentiable if the functions defining the original problem are twice globally differentiable. In addition, the penalty parameter remains finite.
Shih-Ping Han, Olvi L. Mangasarian
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Exactness and algorithm of an objective penalty function
Journal of Global Optimization, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhiqing Meng +4 more
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