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In mathematics, the spectral abscissa of a matrix or a bounded linear operator is the greatest real part of the matrix's spectrum (its set of eigenvalues).^[1] It is sometimes denoted $\alpha (A)$ . As a transformation $\alpha :\mathrm {M} ^{n}\rightarrow \mathbb {R}$ , the spectral abscissa maps a square matrix onto its largest real eigenvalue.^[2]

Matrices

Let λ₁, ..., λ_s be the (real or complex) eigenvalues of a matrix A ∈ C^{n × n}. Then its spectral abscissa is defined as:

\alpha (A)=\max _{i}\{\operatorname {Re} (\lambda _{i})\}\,

In stability theory, a continuous system represented by matrix $A$ is said to be stable if all real parts of its eigenvalues are negative, i.e. $\alpha (A)<0$ .^[3] Analogously, in control theory, the solution to the differential equation ${\dot {x}}=Ax$ is stable under the same condition $\alpha (A)<0$ .^[2]

References

^ Deutsch, Emeric (1975). "The Spectral Abscissa of Partitioned Matrices" (PDF). Journal of Mathematical Analysis and Applications. 50: 66–73 – via CORE.
^ ^a ^b Burke, J. V.; Lewis, A. S.; Overton, M. L. "OPTIMIZING MATRIX STABILITY" (PDF). Proceedings of the American Mathematical Society. 129 (3): 1635–1642.
^ Burke, James V.; Overton, Micheal L. (1994). "DIFFERENTIAL PROPERTIES OF THE SPECTRAL ABSCISSA AND THE SPECTRAL RADIUS FOR ANALYTIC MATRIX-VALUED MAPPINGS" (PDF). Nonlinear Analysis, Theory, Methods & Applications. 23 (4): 467–488 – via Pergamon.

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