A NOTE ON SOME STOCHASTIC APPROACHES TO NONSTOCHASTIC DISCRETE PROGRAMMING.
CARNEGIE INST OF TECH PITTSBURGH PA MANAGEMENT SCIENCES RESEARCH GROUP
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Some ways are proposed for assigning best probabilities to the values assumed by the variables of a deterministic integer linear programming problem. Our results, which can be used in the framework of the Reiter and Rice discrete optimization procedure and the Graves and Whinston confidence level procedure, also give meaningful conditional probabilities and suggest other stochastic approaches to discrete linear programming. Author
- Operations Research