Euler Cutting Planes for Fractional CVaR Programming
Abstract
We maximise expected reward per unit of marginal tail risk, a reward-to-CVaR ratio against a baseline. The standard Dinkelbach route re-solves a Rockafellar--Uryasev LP or QP with one auxiliary variable per tail scenario at each multiplier update. Our method eliminates the multiplier iteration and keeps scenario auxiliaries out of the master, while still evaluating tail risk to generate cuts. A Charnes--Cooper/Schaible homogenisation removes the reward/risk multiplier, and the remaining convex constraint is separated on demand: each cut coefficient is an Euler allocation vector, read off a ranking and an average. The result is a single convex program, finitely terminating at the global optimum, with size independent of S --- an LP in M+1 variables, or a second-order cone program in M+2 for a concave quadratic numerator. The method runs 20--100 times faster than the exact Dinkelbach formulation and only needs single-digit cut counts.
Related
- Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming
- A variational quantum eigensolver-based cutting plane framework for semidefinite programming problems
- Euler Scheme for Stochastic Functional Differential Equations Driven by Fractional Brownian Motion via Fractional Calculus Techniques
- The Numerical Onsager-Machlup action functional for Euler discretized SDEs driven by fractional Brownian motion
- Dynamic programming principle for controlled systems driven by fractional Brownian motions and related backward stochastic Riccati equations
BibTeX
@misc{seshadri2026euler,
title = {{Euler Cutting Planes for Fractional CVaR Programming}},
author = {Pranay Seshadri and Hubert Dzieciol and Zhe Feng and Alan Tua},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14656},
eprint = {2610.14656},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14656},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Euler Cutting Planes for Fractional CVaR Programming
AU - Pranay Seshadri
AU - Hubert Dzieciol
AU - Zhe Feng
AU - Alan Tua
PY - 2026
DA - 2026/10/10
PB - arXiv
DO - 10.48550/arxiv.2610.14656
UR - https://arxiv.org/abs/2610.14656
AB - We maximise expected reward per unit of marginal tail risk, a reward-to-CVaR ratio against a baseline. The standard Dinkelbach route re-solves a Rockafellar--Uryasev LP or QP with one auxiliary variable per tail scenario at each multiplier update. Our method eliminates the multiplier iteration and keeps scenario auxiliaries out of the master, while still evaluating tail risk to generate cuts. A Charnes--Cooper/Schaible homogenisation removes the reward/risk multiplier, and the remaining convex constraint is separated on demand: each cut coefficient is an Euler allocation vector, read off a ranking and an average. The result is a single convex program, finitely terminating at the global optimum, with size independent of S --- an LP in M+1 variables, or a second-order cone program in M+2 for a concave quadratic numerator. The method runs 20--100 times faster than the exact Dinkelbach formulation and only needs single-digit cut counts.
ER -