AI RESEARCH
Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
arXiv CS.LG
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ArXi:2605.14019v1 Announce Type: cross Regret is the cost of uncertainty in algorithmic decision-making. Quantifying regret typically requires computationally expensive simulation via Sample Average Approximation (SAA), with complexity $\mathcal{O}(Bn^{2}d^{3})$ in the number of scenarios $B$, variables $n$, and constraints $d