By Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski
Robust optimization remains to be a comparatively new method of optimization difficulties plagued by uncertainty, however it has already proved so helpful in genuine functions that it's tough to take on such difficulties this present day with no contemplating this robust method. Written by way of the relevant builders of sturdy optimization, and describing the most achievements of a decade of study, this can be the 1st booklet to supply a entire and updated account of the subject.
Robust optimization is designed to satisfy a few significant demanding situations linked to uncertainty-affected optimization difficulties: to function less than loss of complete details at the nature of uncertainty; to version the matter in a sort that may be solved successfully; and to supply promises concerning the functionality of the solution.
The publication starts off with a comparatively uncomplicated therapy of doubtful linear programming, continuing with a deep research of the interconnections among the development of acceptable uncertainty units and the classical probability constraints (probabilistic) procedure. It then develops the strong optimization idea for doubtful conic quadratic and semidefinite optimization difficulties and dynamic (multistage) difficulties. the speculation is supported by way of a variety of examples and computational illustrations.
An crucial ebook for someone engaged on optimization and determination making lower than uncertainty, strong Optimization additionally makes a terrific graduate textbook at the subject.