ROBUST SOLUTIONS OF UNCERTAINTY AFFECTED CONIC OPTIMIZATION PROBLEMS<BR>

Aharon Ben-Tal<BR>

	     MINERVA Optimization Center, TECHNION, Haifa, Israel<BR>

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Abstract:<BR>
	     We survey the main developments in Robust Optimization (RO), a
	     methodology aimed at solving optimization problems (static and dynamic)
	     affected by uncertainty. We focus primarily on issues of
	     computational tractability of the robust counterparts emerging from
	     conic optimization problems (linear, conic quadratic and semidefinite ).
	     Results pertaining to the latter issues are then used to process
	     efficiently probabilistic constraints under partial stochastic
	     information. Finally we discuss the sythesis of uncertainty affected
	     discrete-time linear control systems by the RO methodology and
	     illustrate the results by treating a supply-chain problem.