Introductory Functional Analysis With Applications

By E. Kreyszig. 1978

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Citations in the corpus

Fastfood: Approximate Kernel Expansions in Loglinear Time (2014, background): “The norm (cid:107) λ 1 exists, i.e., k corresponds to a trace class operator Kreyszig (1989).”
Mean Field Multi-Agent Reinforcement Learning (2018, background): “Following the contraction mapping theorem (Kreyszig, 1978), in order to be a contraction, the operator has to satisfy: d(B(a),B(b)) ≤ αd(a, b), ∀a, b where 0 ≤ α 1 and B(a) , [B(a1), . . . ,B(aN )].”
Continuous Nowhere Differentiable Functions (2003, unknown): “See Kreyszig [42], pages 36-37.”
A class of expansive-type Krasnosel’skii fixed point theorems (2009, background): “On the other hand, since S is continuous on K , it implies that τS : K → K is also continuous and τS(K) resides in a compact subset of E, (for a proof of this result in general metric spaces, see [13]).”
Diffusion Adaptation over Networks (2012, background): “To establish part (c), we start by recalling that all norms on finite-dimensional vector spaces are equivalent [20,21].”
Efficiency Bounds for Estimating Linear Functionals of Nonparametric Regression Models with Endogenous Regressors (2007, methodology): “, Stock (1989) estimates the contrast between functionals of E[Y |X] using before-and-after policy intervention data; letting Y denote the market demand and X the price, Newey and McFadden (1994) consider estimating ∫ b a E[Y |X = x] dx, the approximate change in consumer surplus for a given price c”
Numerical treatment for solving fractional Riccati differential equation (2013, background): “[28] Suppose that H is a Hilbert space and U is a closed subspace of H such that dimU <1 and u1, u2, .”
Greed is good: algorithmic results for sparse approximation (2004, unknown): “to the inverse [29].”