V
Vaithilingam Jeyakumar
Researcher at University of New South Wales
Publications - 179
Citations - 5105
Vaithilingam Jeyakumar is an academic researcher from University of New South Wales. The author has contributed to research in topics: Convex analysis & Convex optimization. The author has an hindex of 39, co-authored 176 publications receiving 4671 citations. Previous affiliations of Vaithilingam Jeyakumar include University of Kentucky & University of Mannheim.
Papers
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On generalised convex mathematical programming
Vaithilingam Jeyakumar,B. Mond +1 more
TL;DR: In this paper, a relaxation of the sufficient optimality conditiond duality result for the generalised convex programming problem is proposed. But the relaxation is not applicable to nonlinear multi-objective fractional programming problems.
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New sequential lagrange multiplier conditions characterizing optimality without constraint qualification for convex programs
TL;DR: A new sequential Lagrange multiplier condition characterizing optimality without a constraint qualification for an abstract nonsmooth convex program is presented in terms of the subdifferentials and the $\epsilon$-subdifferentials.
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Strong Duality in Robust Convex Programming: Complete Characterizations
Vaithilingam Jeyakumar,Guoyin Li +1 more
TL;DR: A duality theory for convex programming problems in the face of data uncertainty via robust optimization is presented and strong duality between the robust counterpart of an uncertain convex program and the optimistic counterpart of its uncertain Lagrangian dual is characterized.
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A class of nonconvex functions and mathematical programming
T. Weir,Vaithilingam Jeyakumar +1 more
TL;DR: In this article, a categorisation of fonctions appelees pre-invexes is defined, and conditions d'optimalite and des theoremes de dualite for des programs a valeurs scalaires and a VALEUR scalaires comportant des fonsctions pre-Invexes are presented.
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Approximate Jacobian Matrices for Nonsmooth Continuous Maps and C 1 -Optimization
Vaithilingam Jeyakumar,D. T. Luc +1 more
TL;DR: In this article, the notion of approximate Jacobian matrices is introduced for a continuous vector-valued map, based on the idea of convexificators of real-valued functions.