1 citations · 2 across the 11 of their papers we have counts for
11 papers
Proximal Gradient Methods for Unconstrained Set Optimization Problems with Set-Valued Maps of Finite Cardinality
Ravi Raushan, Debdas Ghosh, Anshika +1
This work presents two different types of proximal gradient methods, with line search and without line search, for solving unconstrained set-valued optimization problems under the…
Hager-Zhang Conjugate Gradient Method for Set Optimization with Set-Valued Objective Map of Finite Cardinality
Ravi Raushan, Debdas Ghosh, Zai-Yun Peng
This work introduces a nonlinear Hager-Zhang conjugate gradient method for solving set optimization problems. The objective function under consideration is defined by a finite coll…
Newton Method for Multiobjective Optimization Problems of Interval-Valued Maps
Tapas Mondal, Debdas Ghosh, Do Sang Kim
In this article, we propose a Newton-based method for solving multiobjective interval optimization problems (MIOPs). We first provide a connection between weakly Pareto optimal poi…
Nonlinear Conjugate Gradient Method for Multiobjective Optimization Problems of Interval-Valued Maps
Tapas Mondal, Debdas Ghosh, Jingxin Liu +1
In this article, we propose an algorithm for the nonlinear conjugate gradient method to find a Pareto critical point of unconstrained multiobjective interval optimization problems.…
Trust-Region Method for Optimization of Set-Valued Maps Given by Finitely Many Functions
Suprova Ghosh, Debdas Ghosh, Christiane Tammer +1
In this article, we develop a trust-region technique to find critical points of unconstrained set optimization problems with the objective set-valued map defined by finitely many t…
Cubic Regularization Technique of the Newton Method for Vector Optimization
Debdas Ghosh
This study proposes a cubic regularization of the Newton method for generating weakly efficient points of unconstrained vector optimization problems under no convexity assumption o…
Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions
Debdas Ghosh, Anshika, Jen-Chih Yao +1
In this article, we propose a quasi-Newton method for unconstrained set optimization problems to find its weakly minimal solutions with respect to lower set-less ordering. The set-…
Nonlinear Conjugate Gradient Methods for Optimization of Set-Valued Mappings of Finite Cardinality
Debdas Ghosh, Ravi Raushan, Zai-Yun Peng +1
This article presents nonlinear conjugate gradient methods for finding local weakly minimal points of set-valued optimization problems under a lower set less ordering relation. The…
Generalized Ordered Weighted Aggregation Robustness to Solve Uncertain Single Objective Optimization Problems
Nand Kishor, Debdas Ghosh, Xiaopeng Zhao
Robust optimization aims to find optimum points from the collection of points that are feasible for every possible scenario of a given uncertain set. An optimum solution to a robus…
Newton Method for Set Optimization Problems with Set-Valued Mapping of Finitely Many Vector-Valued Functions
Debdas Ghosh, Anshika, Qamrul Hasan Ansari +1
In this paper, we propose a Newton method for unconstrained set optimization problems to find its weakly minimal solutions with respect to lower set-less ordering. The objective fu…
Second-Order Strong Optimality and Second-Order Duality for Nonsmooth Constrained Multiobjective Fractional Programming Problems
Jiawei Chen, Luyu Liu, Yibing Lv +2
This paper investigates constrained nonsmooth multiobjective fractional programming problem (NMFP) in real Banach spaces. It derives a quotient calculus rule for computing the firs…