Ordinal optimization
In mathematical optimization, ordinal optimization is the maximization of functions taking values in a partially ordered set ("poset").
Problems of ordinal optimization arise in many disciplines. Ordinal optimization has applications in the theory of queuing networks. In particular, antimatroids and the "max-plus algebra" have found application in network analysis and queuing theory, particularly in queuing networks and discrete-event systems. Computer scientists study selection algorithms, which are simpler than sorting algorithms. Statistical decision theory studies "selection problems" that require the identification of a "best" subpopulation or of identifying a "near best" subpopulation. Partially ordered vector spaces and vector lattices are important in optimization with multiple objectives.
Problems of ordinal optimization arise in many disciplines. Ordinal optimization has applications in the theory of queuing networks. In particular, antimatroids and the "max-plus algebra" have found application in network analysis and queuing theory, particularly in queuing networks and discrete-event systems. Computer scientists study selection algorithms, which are simpler than sorting algorithms. Statistical decision theory studies "selection problems" that require the identification of a "best" subpopulation or of identifying a "near best" subpopulation. Partially ordered vector spaces and vector lattices are important in optimization with multiple objectives.
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