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282. A Subgradient Methods for general invex non-smooth vector optimization problems.
Contributed abstract in session TC-41: Subgradient-based methods, stream Nonsmooth Optimization.
Tuesday, 12:30-14:00Room: 97 (building: 306)
Authors (first author is the speaker)
| 1. | ARUNIMA KUMARI
|
| Mathematics, Bhgwan Parshuram Institute of Technology |
Abstract
Vector Optimization problems have a wide range of applications in various fields, like economics, decision theory, game theory, information theory and optimal control theory. In this paper, unlike the general scalarization techniques, a sub gradient method without a scalarization approach is proposed for minimizing a non-differentiable invex function which works directly with vector valued functions. The proposed method includes regularization and the interior points variants of Newton's Method. The proposed algorithm generates a sequence of efficient points in the interior of epigraph of objective function which satisfy Karush-Kuhn-Tucker conditions.
Keywords
- Convex Optimization
- Multi-Objective Decision Making
- Programming, Multi-Objective
Status: accepted
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