When analyzing systems with a large number of parameters, the dimen-
sion of the original system may present insurmountable difficulties for
the analysis. It may then be convenient to reformulate the original
system in terms of substantially fewer aggregated variables, or
macrovariables. In other words, an original system with an n-dimensional
vector of states is reformulated as a system with a vector of dimension
much less than n. The aggregated variables are either readily defined
and processed, or the aggregated system may be considered as an
approximate model for the orig- inal system. In the latter case, the
operation of the original system can be exhaustively analyzed within the
framework of the aggregated model, and one faces the problems of
defining the rules for introducing macrovariables, specifying loss of
information and accuracy, recovering original variables from aggregates,
etc. We consider also in detail the so-called iterative aggregation
approach. It constructs an iterative process, at- every step of which a
macroproblem is solved that is simpler than the original problem because
of its lower dimension. Aggregation weights are then updated, and the
procedure passes to the next step. Macrovariables are commonly used in
coordinating problems of hierarchical optimization.