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Sufficient Conditions for Aggregation of Linear Programming Models
Author(s) -
Paris Quirino,
Rausser Gordon C.
Publication year - 1973
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1238355
Subject(s) - curse of dimensionality , proportionality (law) , linear programming , homogeneity (statistics) , computer science , mathematical optimization , mathematics , artificial intelligence , machine learning , political science , law
Three sets of sufficient conditions for exact aggregation of linear programming models are discussed. They provide a relaxation of the proportionality and dimensionality conditions among elements of the micro problems required by R. H. Day's specification. For correct aggregation it is not necessary to classify firms according to “homogeneity” criteria.