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In math, science, engineering, and other quantitative fields, solving a problem is all about gathering the right information to be able to find an appropriate answer. With the proper constraints and objectives, a solution can be found. Sometimes, however, there may be problems where constraints or objectives are not exactly known. This complicates the problem-solving process, but there are ways to intelligently estimate a solution or range of potential solutions. Mathematically, this scenario can be addressed through stochastic programming.

Stochastic programming involves using mathematical algorithms to define the uncertainties in a problem as rigorously as possible. Many times, the uncertainty can be represented with a probability distribution. In the real world, these types of problems are often converted to large-scale deterministic problems whenever possible covering a large range of possibilities. Other times, probability can be used to indicate the likelihood of a potential solution. Regardless, stochastic programming often involves the use of actual computer applications to tackle algebraic modeling.