Lagrangian duality for nonconvex optimization problems with abstract convex functions
Optimization and Control
2020-11-19 v1
Abstract
We investigate Lagrangian duality for nonconvex optimization problems. To this aim we use the -convexity theory and minimax theorem for -convex functions. We provide conditions for zero duality gap and strong duality. Among the classes of functions, to which our duality results can be applied, are prox-bounded functions, DC functions, weakly convex functions and paraconvex functions.
Cite
@article{arxiv.2011.09194,
title = {Lagrangian duality for nonconvex optimization problems with abstract convex functions},
author = {Ewa M. Bednarczuk and Monika Syga},
journal= {arXiv preprint arXiv:2011.09194},
year = {2020}
}