Applied Functional Analysis

By T. Fleischer. 2016

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A Nitsche‐type formulation and comparison of the most common domain decomposition methods in isogeometric analysis (2014, background): “/ is the space of all square integrable functions with square integrable derivatives in , see [46] for more details.”
Projected estimators for robust semi-supervised classification (2016, background): “One of the properties for projections onto a convex subspace in a Hilbert space is (Aubin, 2000, Proposition 1.4 for any w ∈ Θ .”
Linear Stochastic Systems: A Geometric Approach to Modeling, Estimation and Identification (2016, unknown): “1), and hence, setting x = (x+, w+) ′,    x(t+ 1) = [ 1 2 5 2 0 0 ] x(t) + [ 0 1 ] w+(t)”