A globally convergent Newton method for convex SC1 minimization problems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Management Science and Operations Research,Control and Optimization
Link
http://link.springer.com/content/pdf/10.1007/BF02193060.pdf
Reference31 articles.
1. Hiriart-Urruty, J. B., Strodiot, J. J., andNguyen, V. H.,Generalized Hessian Matrix and Second-Order Optimality Conditions for Problems with C 1,1 Data, Applied Mathematics and Optimization, Vol. 11, pp. 43–56, 1984.
2. Klatte, D., andTammer, K.,On Second-Order Sufficient Optimality Conditions for C 1,1 Optimization, Optimization, Vol. 19, pp. 169–179, 1988.
3. Kummer, B.,Lipschitzian Inverse Functions, Directional Derivatives, and Application in C 1,1 Optimization, Journal of Optimization Theory and Applications, Vol. 70, pp 559–580, 1991.
4. Han, S. P.,Superlinearly Convergent Variable-Metric Algorithms for General Nonlinear Programming Problems, Mathematical Programming, Vol. 11, pp. 263–282, 1976.
5. Kojima, M., andShindo, S.,Extensions of Newton and Quasi-Newton Methods to Systems of PC 1 Equations, Journal of the Operations Research Society of Japan, Vol. 29, pp. 352–374, 1986.
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