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.
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