A class of nonconvex semidefinite programming in which every KKT point is globally optimal

Akatsuki Nishioka; Yoshihiro Kanno

arxiv: 2506.16739 · v2 · pith:RO4GUQXSnew · submitted 2025-06-20 · 🧮 math.OC

A class of nonconvex semidefinite programming in which every KKT point is globally optimal

Akatsuki Nishioka , Yoshihiro Kanno This is my paper

classification 🧮 math.OC

keywords programmingoptimizationclasseveryfractionalgloballynonconvexoptimal

0 comments

read the original abstract

We consider a special class of nonconvex semidefinite programming problems and show that every point satisfying the Karush--Kuhn--Tucker (KKT) conditions is globally optimal despite nonconvexity. This property is related to pseudoconvex optimization and fractional programming. We also present several applications to robust fractional programming and generalized eigenvalue optimization appearing in topology optimization, network control, finance, etc.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Revisiting Invex Functions: Explicit Kernel Constructions and Characterizations
math.OC 2025-10 unverdicted novelty 6.0

Explicit constructions of kernel functions for invex functions are developed together with a kernel-based characterization of pseudoconvexity and examples of nonsmooth non-pseudoconvex invex functions from signal processing.