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Exact penalty functions and stability in locally Lipschitz programming. (English) Zbl 0587.90083
We 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 is also an isolated local minimum of the exact penalty function. A tight lower bound on the parameter value is provided when certain first order sufficiency conditions are satisfied. We apply these results to unify and extend some results for convex programming. Since several effective algorithms for solving nonlinear programs with differentiable functions rely on exact penalty functions, our results provide a framework for extending these algorithms to problems with locally Lipschitz functions.

MSC:
90C30 Nonlinear programming
90C31 Sensitivity, stability, parametric optimization
49M37 Numerical methods based on nonlinear programming
90C25 Convex programming
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