A
Anurag Jayswal
Researcher at Indian Institutes of Technology
Publications - 96
Citations - 618
Anurag Jayswal is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Duality (optimization) & Duality gap. The author has an hindex of 12, co-authored 89 publications receiving 461 citations. Previous affiliations of Anurag Jayswal include Indian Institute of Technology Dhanbad & Birla Institute of Technology and Science.
Papers
More filters
Journal ArticleDOI
On sufficiency and duality for a class of interval-valued programming problems
TL;DR: Sufficient optimality conditions are established under generalized convex functions for a feasible solution to be an efficient solution and appropriate duality theorems for Mond–Weir and Wolfe type duals are discussed in order to relate the efficient solutions of primal and dual programs.
Journal ArticleDOI
On sufficiency and duality in multiobjective programming problem under generalized α -type I univexity
TL;DR: New classes of generalized α-univex type I vector valued functions are introduced and a number of Kuhn–Tucker type sufficient optimality conditions are obtained for a feasible solution to be an efficient solution.
Journal ArticleDOI
Non-differentiable minimax fractional programming with generalized α-univexity
TL;DR: In this paper, Mishra et al. studied a non-differentiable minimax fractional programming problem under the assumption of generalized @a-univex function and derived Karush-Kuhn-Tucker-type sufficient optimality conditions for the problem and its three different form of dual problems.
Journal ArticleDOI
On interval-valued optimization problems with generalized invex functions
TL;DR: In this article, sufficient optimality conditions are established for LU optimal solution concept under generalized -invexity. And weak, strong and strict converse duality theorems for Wolfe and Mond-Weir type duals are derived in order to relate the LU optimal solutions of primal and dual problems.
Journal ArticleDOI
An exact l1 penalty function method for multi-dimensional first-order PDE constrained control optimization problem
Anurag Jayswal,Preeti +1 more
TL;DR: This paper uses the exact l1 penalty function method to solve a multi-dimensional first-order PDE constrained control optimization problem and shows that an optimal solution is a minimizer of its associated penalized problem under the hypothesis of convex Lagrange functional.