Computational complexity of μ calculation

Abstract

The structured singular value p measures the robustness of uncertain systems. Numerous researchers over the last decade have worked on developing efficient methods for computing p. This paper considers the complexity of calculating p with general mixed real/complex uncertainty in the framework of combinatorial complexity theory. In particular, it is proved that the 11 recognition problem with either pure real or mixed real/complex uncertainty is NP-hard. This strongly suggests that it is futile to pursue exact methods for calculating p of general systems with pure real or mixed uncertainty for other than small problems. IntroductionRobust stability and performance analysis with real parametric and dynamic uncertainties can be naturally formulated as a structured singular value (or p ) problem, where the block structured uncertainty description is allowed t o contain both real and complex blocks. I t is assumed that the reader is familar with this type of robustness analysis, as space constraints preclude covering *supported by the Fannie and John Hertz Foundation 'Chemical Engineering, 210-41, California Institute of Technology, Pasadena, CA 91125, (818)395-4186 $~l e c t r i c d Engineering, 116-81, California Institute of Technology, Pasadena, CA 91125, (818)395-4808 §TO whom correspondence should be addressed: phone (818)395-4186, fax (818)568-8743, e-mail mm@imc.caltech.edu this here. For a collection of papers describing the engineering motivation and the computational approaches, see [3] and the references contained within.In this paper we determine the computational complexity of p calculation with either pure real or mixed real/complex uncertainty. To apply computational complexity theory, we formulate p calculation as a recognition problem (a 'yes' or 'no' problem). We show that this recognition problem is NP-hard, i.e. a t least as hard as the NP-complete problems.The exact consequences of a problem being NP-complete is still a fundamental open question in the theory of computational complexity, and we refer the reader to Garey and Johnson [5] for an in depth treatment of the subject. However, it is generally accepted that a problem being NP-complete means that it cannot be computed in polynomial time in the worst case.It is important to note that being NP-complete is a property of the problem itself, not of any particular algorithm. The fact that the mixed p problem is NP-hard strongly suggests that,given any algorithm to compute p, there will be problems for which the algorithm cannot find the answer in polynomial time.The terminology of computational complexity theory is used extensively in this paper. The definitions for NP-complete, NP-hard, recognition problems, and other terms agree with those in the well-known textbooks by Garey and Johnson [5] and Papadimitriou and Steiglitz 181.The proofs are simple. First we show that indefinite quadratic programming can be cast as a p problem of "roughly" the same size. Since the recognition problem for indefinite quadratic programming is NP-complete, the p ...

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