Problem-based optimal scenario generation and reduction in stochastic programming
Problem-based optimal scenario generation and reduction in stochastic programming is a scholarly work, published in 2018 in ''Mathematical Programming''. The main subjects of the publication include Monetary-disequilibrium theory, stochastic optimization, Stochastic programming, multi-objective optimization, mathematical optimization, Newsvendor model, linear programming, robust optimization, algorithmic stability, dynamic programming, mathematics, computer science, and reduction. The authors show that the latter is convex if either right-hand sides or costs are random and can be transformed into a semi-infinite program in a number of cases.