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Izhar Ahmad

Izhar Ahmad Mail
King Fahd University of Petroleum and Minerals, Saudi Arabia


Department of Mathematics and Statistics


Scopus profile: link

Researcher ID: K-5952-2012

GoogleScholar profile: link


Selected Publications:

1. Verma, K., Verma, J. P., Ahmad, I. (2020). A New Approach on Multiobjective Higher-Order Symmetric Duality Under Cone-Invexity. Bulletin of the Malaysian Mathematical Sciences Society, 44 (1), 479–495. doi: 

2. Ahmad, I., Verma, K., Al-Homidan, S. (2020). Mixed Type Nondifferentiable Higher-Order Symmetric Duality over Cones. Symmetry, 12 (2), 274. doi: 

3. Singh, V., Jayswal, A., Al-Homidan, S., Ahmad, I. (2020). Higher order duality for cone vector optimization problems. Asian-European Journal of Mathematics, 13 (1), 2050020. doi: 

4. Khan, M. A., Ahmad, I., Aljohani, A. (2018). Criterion for Generalized Weakly Fuzzy Invex Monotonocities. Advances in Fuzzy Systems, 2018, 1–9. doi: 

5. Ahmad, I., Kummari, K., Singh, V., Jayswal, A. (2017). Optimality and duality for nonsmooth minimax programming problems using convexifactor. Filomat, 31 (14), 4555–4570. doi: 

6. Jayswal, A., Singh, V., Ahmad, I. (2017). Optimality and duality in multiobjective programming involving higher order semilocally strong convexity. International Journal of Mathematics in Operational Research, 11 (2), 204. doi: 

7. Jayswal, A., Ahmad, I., Banerjee, J. (2015). Nonsmooth Interval-Valued Optimization and Saddle-Point Optimality Criteria. Bulletin of the Malaysian Mathematical Sciences Society, 39 (4), 1391–1411. doi: 

8. Choudhury, S., Jayswal, A., Ahmad, I. (2015). Second order monotonicities and second order variational-like inequality problems. Rendiconti Del Circolo Matematico Di Palermo, 65 (1), 123–137. doi: 

9. Debnath, I. P., Gupta, S. K., Ahmad, I. (2015). A note on strong duality theorem for a multiobjective higher order nondifferentiable symmetric dual programs. OPSEARCH, 53 (1), 151–156. doi: 

10. Tanveer, M., Mangal, M., Ahmad, I., Shao, Y.-H. (2016). One norm linear programming support vector regression. Neurocomputing, 173, 1508–1518. doi: