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2020•arXiv (Cornell University)Open access

Lagrangian duality for nonconvex optimization problems with abstract convex functions

Ewa M. Bednarczuk, Monika Syga

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Abstract

We investigate Lagrangian duality for nonconvex optimization problems. To this aim we use the $Φ$-convexity theory and minimax theorem for $Φ$-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.

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What this paper is about

We investigate Lagrangian duality for nonconvex optimization problems. To this aim we use the $Φ$-convexity theory and minimax theorem for $Φ$-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.

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Available abstract

We investigate Lagrangian duality for nonconvex optimization problems. To this aim we use the $Φ$-convexity theory and minimax theorem for $Φ$-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.

Key concepts: Duality gap, Duality (order theory), Strong duality, Perturbation function, Mathematics, Convexity, Convex analysis, Weak duality

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