Abstrakt
Stochastic programming has seen recent advances with far-reaching impact involving risk measures, distributionally robust optimization, and applications in areas ranging from energy and natural resources to economics and finance to statistics and machine learning. This special issue on stochastic programming includes papers in: (i) risk and distributionally robust optimization; (ii) scenario generation, reduction, and analysis; (iii) asymptotic analysis, including consistency and rates of convergence; and (iv) algorithms, computation, and applications.