Risk-averse optimization is a methodological approach within operations research and decision science focused on making optimal decisions in stochastic systems by explicitly accounting for and mitigating potential downside risks, particularly those associated with low-probability, high-impact events. This field investigates decision problems under uncertainty by employing risk measures or risk-averse utility functions to minimize the potential for unfavorable outcomes, such as significant losses or failures, rather than solely maximizing expected performance. Its significance lies in providing robust decision strategies for critical applications like finance, energy, and supply chain management, where avoiding extreme adverse events is paramount.