Abstract
Inner products are generalizations of the dot product in \(\mathbb {R}^n\). They extend the concept of distance to include orthogonality, with results like Pythagoras’ theorem and the Cauchy-Schwarz inequality. Norms that are induced by inner products are characterized by the parallelogram law and the polarization identity. Complete inner product spaces, called Hilbert spaces, have various special properties, including the least distance theorem for closed convex sets, the Riesz representation theorem on dual spaces, and adjoint operators. Applications are made to least squares approximation and Inverse Problems, with examples from statistics, image reconstruction, tomography, Tikhonov regularization, and Wiener deconvolution. The chapter closes with a section on orthogonal bases, a generalization of Fourier series and the Parseval identity, from which follows that every separable Hilbert space is isomorphic to \(\ell ^2\). Further applications include time-frequency and wavelet bases, solving infinite dimensional linear equations, Gaussian quadrature, and the JPEG image format.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In the mathematical literature, the inner product is often taken to be linear in the first variable; this is a matter of convention. The choice adopted here is that of the “physics” community; it makes many formulas, such as the definition \(x^*(y) := {{\langle } x,y {\rangle }} \), more natural and conforming with function notation.
- 2.
More properly called orthogonal isomorphisms when the space is real.
References
Devaney A (2012) Mathematical foundations of imaging, tomography and wavefield inversion. Cambridge University Press, Cambridge
Walker JS (2008) A primer on wavelets and their scientific applications, 2nd edn. Chapman and Hall/CRC, Boca Raton
Carleson L (1966) On convergence and growth of partial sums of Fourier series. Acta Math 116(1–2):135–137
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Muscat, J. (2024). Hilbert Spaces. In: Functional Analysis. Springer, Cham. https://doi.org/10.1007/978-3-031-27537-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-27537-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27536-4
Online ISBN: 978-3-031-27537-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)