Pulse Explore Journal Club Debates Trending Researchers Journals

Join discussions, follow papers, and never miss your next session.

Download on theApp Store

Home Explore Journal Club Trending

⌘+K

The risk quadrangle in optimization: An overview with recent results and extensions | Synapse

June 3, 2026EURO Journal on Computational Optimization0 citationsOpen Access

View Full Paper

The risk quadrangle in optimization: An overview with recent results and extensions

BGBogdan Grechuk AMAnton Malandii RRR. T. Rockafellar

Key Points

This research aims to provide a comprehensive overview of the Risk Quadrangle and its applications in optimization.
Survey of developments in the Risk Quadrangle since 2013.
Unification of various risk measures and their axioms.
Demonstrations in portfolio optimization, regression, and classification.
Introduced subregularity axioms that relax classical principles.
Linked statistical divergences to Risk Quadrangle duality, encompassing distributionally robust optimization (DRO).
Provided insights into application areas such as portfolio optimization and classification problems.

Abstract

• Unified survey of the Risk Quadrangle (since 2013). • Unifies risk, deviation, regret, error, and the shared statistic. • Subregularity axioms relax classical regularity and coherency. • Links statistical divergences to RQ duality, unifying DRO. • Demonstrations in portfolio optimization, regression, classification.

Ask AI

Helpful

Bookmark

Share

View Full Paper

Ask AI

Helpful

Bookmark

Share

View Full Paper

Cite This Study

Grechuk et al. (Mon,) studied this question.

synapsesocial.com/papers/6a1fc550dee9eb8c0dce6b7f https://doi.org/https://doi.org/10.1016/j.ejco.2026.100129