What is it about?
Stochastic Programming is an extension of linear optimization models in which we allow us to identify optimal choices in the face of uncertainty.
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Why is it important?
Linear programming is one of the basic building blocks available for deterministic optimization models. However, this approach assumes that data for the model is known with certainty. However, there are many circumstances in which data is used for the model is uncertainty. This approach accommodates such data uncertainty.
Perspectives
Stochastic Programming (SP) models have much in common with approaches used in statistical and machine learning (e.g. expected risk minimization). For example, support vector machines (SVM) can be written as an SP model. The paper provides a brief introduction to SP models, and discusses several alternative models which come under this rubric of decisions under uncertainty.
Dr. Suvrajeet Sen
University of Arizona
Read the Original
This page is a summary of: Stochastic programming, Springer Science + Business Media,
DOI: 10.1007/1-4020-0611-x_1005.
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