Rupesh Krishna Pandey
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Rupesh Krishna Pandey has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Balendu Bhooshan Upadhyay
Department of Mathematics, Indian Institute of Technology Patna, Patna 801103, Bihar, India
6 shared publications - Subham Poddar
Department of Mathematics, Indian Institute of Technology Patna, Patna, India
4 shared publications - Shengda Zeng
National Center for Applied Mathematics in Chongqing, School of Mathematical Sciences, Chongqing Normal University, Chongqing, China
3 shared publications - Ioan M. Stancu - Minasian
“Gheorghe Mihoc-Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, 050711 Bucharest, Romania
2 shared publications - Atefeh Hassani Bafrani
Department of Mathematics, Payame Noor University, Tehran, Iran
1 shared publication - Giuseppe Caristi
University of Messina
1 shared publication
Latest Publications
- 10 March 2026
- Published by Springer Nature in Annals of Operations Research
- Vol. 359 (3) , 2389-2429
Fletcher-reeves-type conjugate direction algorithm for interval-valued multiobjective optimization problems
- 10 March 2026
- Published by Springer Nature in Annals of Operations Research
- Vol. 359 (3) , 2389-2429
Fletcher-reeves-type conjugate direction algorithm for interval-valued multiobjective optimization problems
- Open Access
- 18 August 2025
- Published by MDPI AG in Preprints.org
Conjugate Descent-Type Algorithm for Interval-valued Multiobjective Optimization Problems
This paper deals with a class of interval-valued multiobjective optimization problems (IVMOPs). The conjugate-descent (CD) direction is defined to introduce the CD-type conjugate direction algorithm for IVMOPs. We perform the convergence analysis and establish that the algorithm exhibits a linear rate of convergence under certain suitable assumptions. We further investigate its worst-case complexity. Several numerical examples, including a large-scale problem, are provided to demonstrate the efficacy of the proposed algorithm. To the best of our knowledge, the CD-type conjugate direction algorithm is introduced for the first time to solve a class of IVMOPs.
- 18 August 2025
- Published by MDPI AG in Preprints.org
Open AccessConjugate Descent-Type Algorithm for Interval-valued Multiobjective Optimization Problems
This paper deals with a class of interval-valued multiobjective optimization problems (IVMOPs). The conjugate-descent (CD) direction is defined to introduce the CD-type conjugate direction algorithm for IVMOPs. We perform the convergence analysis and establish that the algorithm exhibits a linear rate of convergence under certain suitable assumptions. We further investigate its worst-case complexity. Several numerical examples, including a large-scale problem, are provided to demonstrate the efficacy of the proposed algorithm. To the best of our knowledge, the CD-type conjugate direction algorithm is introduced for the first time to solve a class of IVMOPs.
- Open Access
- 23 June 2025
- Published by MDPI AG in Algorithms
- Vol. 18 (7) , 381
Hestenes–Stiefel-Type Conjugate Direction Algorithm for Interval-Valued Multiobjective Optimization Problems
This article investigates a class of interval-valued multiobjective optimization problems (IVMOPs). We define the Hestenes–Stiefel (HS)-type direction for the objective function of IVMOPs and establish that it has a descent property at noncritical points. An Armijo-like line search is employed to determine an appropriate step size. We present an HS-type conjugate direction algorithm for IVMOPs and establish the convergence of the sequence generated by the algorithm. We deduce that the proposed algorithm exhibits a linear order of convergence under appropriate assumptions. Moreover, we investigate the worst-case complexity of the sequence generated by the proposed algorithm. Furthermore, we furnish several numerical examples, including a large-scale IVMOP, to demonstrate the effectiveness of our proposed algorithm and solve them by employing MATLAB. To the best of our knowledge, the HS-type conjugate direction method has not yet been explored for the class of IVMOPs.
- 23 June 2025
- Published by MDPI AG in Algorithms
- Vol. 18 (7) , 381
Open AccessHestenes–Stiefel-Type Conjugate Direction Algorithm for Interval-Valued Multiobjective Optimization Problems
This article investigates a class of interval-valued multiobjective optimization problems (IVMOPs). We define the Hestenes–Stiefel (HS)-type direction for the objective function of IVMOPs and establish that it has a descent property at noncritical points. An Armijo-like line search is employed to determine an appropriate step size. We present an HS-type conjugate direction algorithm for IVMOPs and establish the convergence of the sequence generated by the algorithm. We deduce that the proposed algorithm exhibits a linear order of convergence under appropriate assumptions. Moreover, we investigate the worst-case complexity of the sequence generated by the proposed algorithm. Furthermore, we furnish several numerical examples, including a large-scale IVMOP, to demonstrate the effectiveness of our proposed algorithm and solve them by employing MATLAB. To the best of our knowledge, the HS-type conjugate direction method has not yet been explored for the class of IVMOPs.
- 28 October 2024
- Published by Springer Nature in Numerical Algorithms
- p. 1-31
On conjugate direction-type method for interval-valued multiobjective quadratic optimization problems
- 28 October 2024
- Published by Springer Nature in Numerical Algorithms
- p. 1-31
On conjugate direction-type method for interval-valued multiobjective quadratic optimization problems
- 2 July 2024
- Published by Elsevier in Fuzzy Sets and Systems
- Vol. 492
A generalization of generalized Hukuhara Newton's method for interval-valued multiobjective optimization problems
- 2 July 2024
- Published by Elsevier in Fuzzy Sets and Systems
- Vol. 492
A generalization of generalized Hukuhara Newton's method for interval-valued multiobjective optimization problems