From the 2 of 8 linked papers with an AI index.
8 papers
Proximal Gradient Methods for Unconstrained Set Optimization Problems with Set-Valued Maps of Finite Cardinality
Ravi Raushan, Debdas Ghosh, Anshika +1
The paper proposes two proximal gradient algorithms (with and without an Armijo‑type line search) for unconstrained set‑valued optimization problems with finitely many component fu…
Hager-Zhang Conjugate Gradient Method for Set Optimization with Set-Valued Objective Map of Finite Cardinality
Ravi Raushan, Debdas Ghosh, Zai-Yun Peng
The paper proposes a nonlinear Hager‑Zhang conjugate gradient algorithm for set optimization problems with a finite collection of differentiable objective functions, establishing W…
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…