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On Optimality Conditions for Mathematical Programming Problems Based on Strong Subdifferentials
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DOI：10.1007/s11228-026-00802-9.png)
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Abstract
En 中文
We develop refined Karush-Kuhn-Tucker (KKT) and Fritz-John (FJ)-type optimality conditions for nonsmooth, nonconvex mathematical programming problems. We pay special attention in the case that the functional constraint belongs to a specific class of generalized convex functions known as strongly quasiconvex functions. After analyzing a specialized subdifferential, named the strong subdifferential, we compute the normal cone of the supremum function in terms of such subdifferentials, and apply this result to the mathematical programming problem. We illustrate our important results by examples.
Keywords:
Nonconvex nonsmooth optimization
Generalized convexity
KKT optimality
FJ optimality
Journal
S
IF:
1.1
Papers:
35
Citations:
0