Rigidity of the structured singular value and applications

Abstract

The structured singular value μE for a linear subspace E of Mn( C) is defined by \[ μE(A)=1 / ∈f\\|X\| \ : \ X ∈ E, \ (In-AX)=0 \ (A ∈ Mn(C)), \] and μE(A)=0 if there is no X ∈ E with (In-AX)=0. It is well-known that μE(A) coincides with the spectral radius r(A) when E=\cIn: c ∈ C \ and μE(A)=\|A\| when E=Mn( C), for all A∈ Mn( C). Also, for any linear subspace E satisfying \cIn: c ∈ C \ ⊂eq E ⊂eq Mn( C), we have r(A)≤ μE(A) ≤ \|A\|. We prove that if E=\cIn: c ∈ C \ and F is any linear subspace of Mn( C) containing E, then μE=μF if and only if E=F. We prove the exact same rigidity theorem for the linear subspace consisting of the diagonal matrices of order n. On the contrary, when E=Mn( C), we show that there is a proper subspace F of Mn( C), viz. the space of symmetric matrices such that μE=μF= operator norm. Further, we characterize all linear subspaces F⊂eq Mn( C) such that μF coincides with the operator norm. Next, we show that in general there is no subspace E of Mn( C) such that μE= the numerical radius, not even for M2( C). We establish the rigidity of the structured singular value for each of the subspaces E of M2( C) such that the corresponding μE-unit ball induces the domains -- symmetrized bidisc, tetrablock, pentablock, hexablock.

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