Nonconvex Homogeneous Optimization: a General Framework and Optimality Conditions of First and Second-Order

Authors

  • Fabián Flores-Bazán, Adrian Carrillo-Galvez Author

Keywords:

Nonconvex optimization, homogeneous functions, copositivity, hidden convexity, strong duality, S-lemma.

Abstract

This work discusses and analyzes a class of nonconvex homogeneous optimization problems, in which the objective function is a positively homogeneous function with a certain degree, and the constraints set is determined by a single homogeneous function with another degree, and a geometric set which is a (not necessarily convex) closed cone. Once a Lagrangian dual problem is associated, it is provided various characterizations for the validity of strong duality property: one of them is related to the convexity of a certain image of the geometric set involving both homogeneous functions, so revealing a hidden convexity. We also derive a suitable S-lemma. In the case where both functions are of the same degree of homogeneity, a copositive reformulation of the original problem is established. It is also established zero-, first- and second-order optimality conditions; KKT (local or global) optimality, giving rise to the notion of L-eigenvalues with applications to symmetric tensors eigenvalues analysis

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Published

2024-01-05

How to Cite

Nonconvex Homogeneous Optimization: a General Framework and Optimality Conditions of First and Second-Order. (2024). Minimax Theory and Its Applications, 9(1), 85–115. https://journalmta.com/index.php/jmta/article/view/118