Simplifying Complexity: Scenario Reduction Techniques in Stochastic Programming

Abstract

Stochastic programming problems arise as mathematical models for optimizing problems under stochastic uncertainty. Computational approaches for solving these models often involve approximating the underlying probability distribution with a probability measure that has finite support. To mitigate the computational complexity associated with increasing the number of scenarios, it may be necessary to reduce their quantity. The scenario is selected as the first element of supp , and the separable structure is used to determine the second element of supp while keeping the first element fixed. The process is repeated to establish the remaining indices, and each subsequent scenario is reduced accordingly. This iterative process continues until scenario is reduced