Motivated by the ideas of asynchronous relaxation algorithms this paper inv
estigates optimal decision-making problems that exhibit decentralized chara
cteristics. Such problems consist of a collection of interacting sub-system
s, each one described by local properties and dynamics, joined together by
the need to accomplish a common task which achieves overall optimal perform
ance. Special properties of such systems that make them ideally suited for
the framework of asynchronous computing are (a) the lack of a single overal
l objective describing the collective performance, and (b) the asynchronism
in implementing topologically optimal decisions based on information which
is local in space and time. A methodology for decentralized decision makin
g is developed based on the solution of a series of sub-problems in which e
ach minimizes a local objective while maximizing a common Lagrangian functi
on, by generating independent approximations of an ascent direction in the
space of the dual variables. The concepts are illustrated by means of motiv
ating examples. (C) 1999 Elsevier Science Ltd. All rights reserved.