One to beat them all: “RYU” – a unifying framework for the construction of safe balls
In this paper, we present a new framework, called “RYU”, for constructing “safe” regions – specifically, sets that are guaranteed to contain the dual solution of a target optimization problem. Our framework applies to the standard case where the objective function is composed of two components: a closed, proper, convex function with Lipschitz-smooth gradient and another closed, proper, convex function. We show that the RYU framework not only encompasses but also improves upon the state-of-the-art methods proposed over the past decade for this class of optimization problems.
Authors
- Clément Elvira (ORCID: https://orcid.org/0000-0001-5316-2191)
- Cédric Herzet (ORCID: https://orcid.org/0000-0003-3039-2669)
- Hong-Phuong Dang
- T. M. Tran (ORCID: https://orcid.org/0009-0005-5999-880X)
Institutions
- Supélec (FR)
- Centre National de la Recherche Scientifique (FR)
- Can Tho University (VN)
- Centre de Recherche en Économie et Management (FR)
- Institut d'Électronique et des Technologies du numéRique (FR)
- Institut de recherche mathématique de Rennes (FR)
- Université de Rennes (FR)
Publication Details
- Journal
- Open Journal of Mathematical Optimization
- Published
- 2025-09-24
- DOI
- https://doi.org/10.5802/ojmo.45
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bộ Giáo dục và Ðào tạo