Abstract
We study the relation between the solutions of two minimization problems with indefinite quadratic forms. We show that a complete link between both solutions can be established by invoking a fundamental set of inertia conditions. While these inertia conditions are automatically satisfied in a standard Hilbert space setting, which is the case of classical least-squares problems in both the deterministic and stochastic frameworks, they nevertheless turn out to mark the differences between the two optimization problems in indefinite metric spaces. Applications to H∞-filtering, robust adaptive filtering, and approximate total-least-squares methods are included.
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© 1996 Birkhäuser Verlag Basel/Switzerland
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Sayed, A.H., Hassibi, B., Kailath, T. (1996). Inertia Conditions for the Minimization of Quadratic Forms in Indefinite Metric Spaces. In: Gohberg, I., Lancaster, P., Shivakumar, P.N. (eds) Recent Developments in Operator Theory and Its Applications. Operator Theory Advances and Applications, vol 87. Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-9035-9_15
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DOI: https://doi.org/10.1007/978-3-0348-9035-9_15
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