Robust stochastic optimisation methods seek decision rules that perform reliably under both inherent randomness and ambiguity in probability models. Combining classical stochastic programming—where ...

Real-valued functions of complex arguments violate the Cauchy-Riemann conditions and, consequently, do not have Taylor series expansion. Therefore, optimization methods based on derivatives cannot be ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...