2025/02/24 by Rajmadan Lakshmanan, Lakshmanan, Rajmadan, Alois Pichler +1
Computer Science · Decision Sciences · #60E15 #90C15 #91B06 #Constraint Satisfaction and Optimization #FOS: Mathematics #Optimization and Control (math.OC) #Risk and Portfolio Optimization #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2502.17043
openalex publication_date 2025/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stochastic dominance is a fundamental concept in decision-making under uncertainty and quantitative finance, yet its practical application is hindered by computational intractability due to infinitely many constraints. We introduce the Julia package StochasticDominance, an open-source tool that efficiently verifies and optimizes under higher-order stochastic dominance constraints. Our approach builds on recent theoretical advancements that reduce infinite constraints to a finite number, making higher-order stochastic dominance more accessible. This package provides a user-friendly, black-box solution, enabling researchers and practitioners to incorporate stochastic dominance constraints seamlessly into their optimization frameworks.