2319. An Iterative Process for Non-smooth Multi-objective Optimization Problem Via Subdifferential Set Approximation
Invited abstract in session TD-51: Advances in nonlinear multiobjective optimization, stream Multiobjective and vector optimization.
Tuesday, 14:30-16:00Room: Parkinson B22
Authors (first author is the speaker)
| 1. | DINESH KUMAR
|
| Mathematics, Indian Institute of Technology Kharagpur | |
| 2. | Geetanjali Panda
|
| Mathematics, Indian Institute of Technology, Kharagpur, INDIA |
Abstract
This work presents an iterative technique for unconstrained multi-objective optimization problems. The objective functions are assumed to be convex but need not be differentiable. This technique provides a sequence of points that converge to its Pareto critical point (a necessary condition of a weak Pareto optimal point) considering reasonable assumptions. Initially, an appropriate subproblem is presented at each iteration in order to find the direction vector by utilizing the sub-differential of each objective function at that point. Subsequently, the method is updated in order to determine the descent direction in an explicit form. A set of numerical examples is presented to validate the suggested approach. This iterative scheme does not rely on any predetermined parameters.
Keywords
- Non-smooth Optimization
- Programming, Multi-Objective
- Convex Optimization
Status: accepted
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