Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-19T22:49:55.607Z Has data issue: false hasContentIssue false

Generalised hessian, max function and weak convexity

Published online by Cambridge University Press:  17 April 2009

X. Q. Yang
Affiliation:
Department of Mathematics, The University of Western Australia, Nedlands, QA 6009 Australia
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper, a second-order characterisation of η-convex C1, 1 functions is derived in a Hilbert space using a generalised second-order directional derivative. Using this result it is then shown that every C1, 1 function is locally weakly convex, that is, every C1, 1 real-valued function f can be represented as f (x) = h (x) − η‖x2 on a neighbourhood of x where h is a convex function and η > 0. Moreover, a characterisation of the generalised second-order directional derivative for max functions is given.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1996

References

[1]Aubin, J.P. and Ekeland, I., Applied nonlinear analysis (John Wiley, New York, 1984).Google Scholar
[2]Auslender, A., ‘Penalty methods for computing points that satisfy second order necessary conditions’, Math. Programming 17 (1979), 229238.Google Scholar
[3]Hiriart-Urruty, J.B., ‘Generalized differentiability, duality and optimization for problems dealing with difference of convex functions’, in Convexity and duality and optimization, (Ponstein, J., Editor), Lecture Notes in Economics and Mathematical Systems, 256, 1984, pp. 3770.Google Scholar
[4]Hiriart-Urruty, J.B., Strodiot, J.J. and Nguyen, V. Hien, ‘Generalized Hessian matrix and second-order optimality conditions for problems with C 1, 1 data’, Appl. Math. Optim. 11 (1984), 4356.Google Scholar
[5]Holmes, R.B., Geometric functional analysis and its applications (Springer-Verlag, Berlin, Heidelberg, New York, 1975).Google Scholar
[6]Jeyakumar, V., ‘On subgradient duality with strong and weak convex functions’, J. Austral. Math. Soc. Ser. A 40 (1986), 143152.CrossRefGoogle Scholar
[7]Michel, P. and Penot, J.P., ‘A generalized derivative for calm and stable functions’, Differential Integral Equations 5 (1991), 433454.Google Scholar
[8]Rockafellar, R.T., ‘Favorable classes of Lipschitz-continuous functions in subgradient optimization’, in Progress in Nondifferentiable Optimization, (Nurminski, E.A., Editor) (Internat. Inst. Appl. Systems. Anal., Laxenburg, 1982), pp. 125143.Google Scholar
[9]Rockafellar, R.T., ‘First- and second-order epi-differentiability in nonlinear programming’, Trans. Amer. Math. Soc. 307 (1988), 75108.Google Scholar
[10]Vial, J.P., ‘Strong and weak convexity of sets and functions’, Math. Oper. Res. 8 (1983), 231259.CrossRefGoogle Scholar
[11]Yang, X.Q. and Jeyakumar, V., ‘Generalized second-order directional derivatives and optimization with C 1, 1 functions’, Optimization 26 (1992), 165185.Google Scholar
[12]Yang, X.Q., ‘Generalized second-order directional derivatives and optimality conditions’, Nonlinear Anal. 22 (1994), 767784.Google Scholar
[13]Yang, X.Q., ‘Smoothing approximations to nonsmooth optimization problems’, J. Austral. Math. Soc. Ser. B 36 (1994), 113.Google Scholar
[14]Yang, X.Q., ‘An exterior point method for computing points that satisfy second-order necessary conditions for a C 1, 1 optimization problem’, J. Math. Anal. Appl. 187 (1994), 118133.Google Scholar