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Introduction to Hilbert Space and the Theory of Spectral Multiplicity: Second Edition
Overview
Suitable for advanced undergraduates and graduate students in mathematics, this volume's sole prerequisite is a background in measure theory. The distinguished mathematician E. R. Lorch praised the book in the Bulletin of the American Mathematical Society as "an exposition which is always fresh, proofs which are sophisticated, and a choice of subject matter which is certainly timely."
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Product Details
| ISBN-13: | 9780486817330 |
|---|---|
| Publisher: | Dover Publications |
| Publication date: | 12/13/2017 |
| Series: | Dover Books on Mathematics |
| Pages: | 128 |
| Sales rank: | 1,142,037 |
| Product dimensions: | 6.00(w) x 9.00(h) x (d) |
About the Author
Hungarian-born Paul R. Halmos (1916–2006) is widely regarded as a top-notch expositor of mathematics. He taught at the University of Chicago and the University of Michigan as well as other universities and made significant contributions to several areas of mathematics, including mathematical logic, probability theory, ergodic theory, and functional analysis.
Read an Excerpt
CHAPTER 1
THE GEOMETRY OF HILBERT SPACE
§1. Linear Functionals
Throughout this book we shall work with vector spaces over the field of complex numbers, or, as they may be more briefly described, complex vector spaces. The simplest and yet by far the most important example of a complex vector space is the set C of all complex numbers, with the vector operations of addition and scalar multiplication interpreted as the ordinary arithmetic operations of addition and multiplication of complex numbers.
We recall an elementary definition. A linear transformation from a complex vector space D to a complex vector space R is a mapping A from D into R such that A (αx + βy) = αAx + βAy identically for all complex numbers α and β and all vectors x and y in D. Just as the special vector space C plays a distinguished role among all complex vector spaces, similarly linear transformations whose range space R coincides with C (such linear transformations are called linear functionals) play a distinguished role among all linear transformations. Explicitly: a linear functional on a complex vector space D is a complex-valued function [xi] on D such that (and now we proceed, for the sake of variety, to state the definition of linearity in terms slightly different from the ones used above)
(i) [xi] is additive (i.e. [xi](x + y) = [xi](x) + %(y) for every pair of vectors x and y in D), and
(ii) [xi] is homogeneous (i.e. [xi](ax) = [xi]) for every complex number α and for every vector x in D).
It is sometimes convenient to consider, along with linear functionals, the closely related conjugate linear functionals whose definition differs from the one just given in that the equation [xi](αx) = α[xi](x) is replaced by [xi](αx) = α*[xi](x). There is a simple and obvious relation between the two concepts: a necessary and sufficient condition that a complex-valued function [xi] on a complex vector space be a linear functional is that [xi]* be a conjugate linear functional.
§2. Bilinear Functionals
For the theory that we shall develop, the concept of a bilinear functional is even more important than that of a linear functional. A bilinear functional on a complex vector space D is a complex-valued function ψ on the Cartesian product of D with itself such that if [xi]y(x) = ηx(y) = ψ(x, y), then, for every x and y in D, [xi]y is a linear functional and ηx is a conjugate linear functional.
This definition of a bilinear functional is different from the one commonly used in the theory of vector spaces over an arbitrary field; the usual definition requires that, for every x and y in D, both ηx and [xi]y shall be linear functionals. An example of a bilinear functional in this "usual" sense may be manufactured by starting with two arbitrary linear functionals [xi] and η and writing ψ(x, y) = [xi](x)η(y); an obviously related example of a bilinear functional in the sense in which we defined that concept is obtained by writing ψ(x, y) = [xi](x])η*(y). The objects that we defined are sometimes called Hermitian bilinear functionals. Further examples of either usual or Hermitian bilinear functionals may be constructed by forming finite linear combinations of examples of the product type described above. After this brief comment on the peculiarity of our terminology (adopted for reasons of simplicity), we shall consistently stick to the definition that was formally given in the preceding paragraph.
It is easy to verify that if ψ is a bilinear functional and if the function ψ is defined by ψ(x, y) = ψ*(y, x), then ψ is a bilinear functional. A bilinear functional ψ is symmetric if ψ = ψ or, explicitly, if ψ(x, y) = ψ*(y, x) for every pair of vectors x and y. A bilinear functional ψ is positive if ψ(x, x) [??] 0 for every vector x, we shall say that ψ is strictly positive if ψ(x, x) > 0 whenever x ≠ 0.
§3. Quadratic Forms
The quadratic form [??] induced by a bilinear functional ψ on a complexvector space is the function defined for each vector x by [??](x) = ψ(x, x). Using this language and notation, we may paraphrase one of the definitions in the last paragraph of the preceding section as follows: ψ is positive if and only if [??] is positive in the ordinary sense of taking only positive values.
A routine computation yields the following useful result.
Theorem 1. If [??] is the quadratic form induced by a bilinear functional ψ on a complex vector space D, then
[MATHEMATICAL EXPRESSION OMITTED]
for every pair of vectors x and y in D.
The process of calculating the values of the bilinear functional ψ from the values of the quadratic form [??], in accordance with the identity in Theorem 1, is known as polarization. As an immediate corollary of this process we obtain (and we state in Theorem 2) the fact that a bilinear functional is uniquely determined by its quadratic form.
Theorem 2. If two bilinear functionals ψ and ψ are such that [??] = [??] then ψ = ψITL.
Theorem 2 in turn may be applied to yield a simple characterization of symmetric bilinear functionals.
Theorem 3. A bilinear functional ψ is symmetric if and only if [??] is real.
Proof. If ψ is symmetric, then [??](x) = ψ(x, x) = ψ*(x, x) = [??]*(x) for all x. If, conversely, [??] is real, then the bilinear functional ψ, defined by ψ(x, y) = ψ*(y, x), and the bilinear functional ψ are such that [??] = [??]; it follows from Theorem 2 that ψ = ψ.
§4. Inner Product and Norm
An inner product in a complex vector space D is a strictly positive? symmetric, bilinear functional on D. An inner product space is a complex vector space D and an inner product in D. The vector space C of all complex numbers becomes an inner product space if the inner product of [α and β is defined to be αβ*; in what follows we shall always interpret the symbol C, not merely as a vector space, but as an inner product space with this particular inner product.
It is convenient and, as it turns out, not confusing to use the same notation for inner product in all inner product spaces; the value of the inner product at an ordered pair of vectors x and y will be denoted by (x, y). The quadratic form induced by the inner product also has a universal symbol: its value at a vector x will be denoted by ||x||2. The positive square root ||x|| of ||x]]2 is called the norm of the vector x. Note that the norm of a vector a in the inner product space C coincides with the absolute value of the complex number α.
Throughout this book, unless in some special context we explicitly indicate otherwise, the symbol D will denote a fixed inner product space; all apparently homeless vectors will be presumed to belong to D and the definitions of all concepts and the proofs of all theorems will pertain to D.
Theorem 1. A necessary and sufficient condition that x = 0 is that (x, y) = 0 for all y.
Proof. If (x, y) = 0 for all y, then, in particular, (x, x) = 0 and consequently, since the inner product is strictly positive, x = 0. If, conversely, x = 0, then (x, y) = (0x, y) = 0(x, y) = 0. (Note that the proof of the converse is nothing more than the proof of the fact that if [xi] is any linear functional, then [xi](0) = 0. It follows, of course, that if ψ is any bilinear functional, then ψ(0, y) = ψ(x, 0) = 0 for all x and y)
Theorem 2. (The parallelogram law.) For any vectors x and y,
[MATHEMATICAL EXPRESSION OMITTED]
Proof. Compute.
The reader should realize the relation between Theorem 2 and the assertion that the sum of the squares of the two diagonals of a parallelogram is equal to the sum of the squares of its four sides.
The most important relation between vectors of an inner product space is orthogonality; we shall say that x is orthogonal to y) in symbols x [perpendicular to] y, if (x, y) = 0. In terms of this concept Theorem 1 says that the only vector orthogonal to every vector is 0. For orthogonal vectors the statement of the parallelogram law may be considerably sharpened.
Theorem 3. (The Pythagorean theorem.) If x [perpendicular to] y, then
[MATHEMATICAL EXPRESSION OMITTED]
The reader should realize the relation between Theorem 3 and the assertion that the square of the hypotenuse of a right triangle is the sum of the squares of its two perpendicular sides.
A family {xj} of vectors is an orthogonal family if xj [perpendicular to] xk whenever j ≠ k. We shall have no qualms about using the obvious inductive generalization of the Pythagorean theorem, i.e. the assertion that if {xj} is a finite orthogonal family, then [MATHEMATICAL EXPRESSION OMITTED].
§5. The Inequalities of Bessel and Schwarz
A vector x is normalized, or is a unit vector, if || x || = 1; the process of replacing a non-zero vector x by the unit vector x/|| x || is called normalization. A family {xj} of vectors is an orthonormal family if it is an orthogonal family and each vector Xj is normalized, or, more explicitly, if (xj, Xk) = δjk for all j and k.
Theorem 1. (Bessel's inequality.) If {xj} is a finite orthonormal family of vectors, then
[MATHEMATICAL EXPRESSION OMITTED]
for every vector x.
Proof.
[MATHEMATICAL EXPRESSION OMITTED]
(The expressions (x, xj) will occur frequently in our work; they are called the Fourier coefficients of the vector x with respect to the orthonormal family {xj}.)
It is sometimes useful to realize that the strict positiveness of the inner product is not needed to prove the Bessel inequality. In the presence of strict positiveness, however, the statement of Bessel's inequality can be improved by adding to it the assertion that equality holds if and only if x is a linear combination of the xj's. The proof of this addition is an almost immediate consequence of the observation that in the proof of Bessel's inequality there is only one place at which an inequality sign occurs.
Theorem 2. (Schwarz's inequality.) |(x, y) | [??] || x || • || y ||.
Proof. If y = 0, the result is obvious. If y ≠ 0, write y0 = y/|| y ||; since || y0 || = 1, i.e. since the family consisting of the one term y0 is an orthononormal family, it follows from Bessel's inequality that | (x, y0) | [??] || x ||.
Schwarz's inequality, just as Bessel's inequality, would be true even if the inner product were not strictly positive (but merely positive). Our proof of Schwarz's inequality is not delicate enough to yield this improvement: we made use of strict positiveness through the possibility of normalizing any non-zero vector. In the presence of strict positiveness, however, the statement of Schwarz's inequality can be improved by adding to it the assertion that equality holds if and only if x and y are linearly dependent; the proof of this addition is, in one direction, trivial and, in the other direction, a consequence of the corresponding facts about Bessel's inequality.
The Schwarz inequality has an interesting generalization. If {xj} is a non-empty, finite family of vectors, and if γjk = (xj, xk), then the determinant of the matrix [γjk] is non-negative; it vanishes if and only if the xj's are linearly dependent.
§6. Hilbert Space
Theorem 1. The norm in an inner product space is
strictly positive (i.e. || x || > 0 whenever x ≠ 0), positively homogeneous (i.e. || αx || = | α | • || x ||), and subadditive (i.e. || x + y || [??] || x || + || y ||).
Proof. The strict positiveness of the norm is merely a restatement of the strict positiveness of the inner product. The positive homogeneity of the norm is a consequence of the identity
[MATHEMATICAL EXPRESSION OMITTED]
The subadditivity of the norm follows, using Schwarz's inequality, from the relations
[MATHEMATICAL EXPRESSION OMITTED]
Theorem 2. If the distance from a vector x to a vector y is defined to be || x - y ||, then, with respect to this distance function, D is a metric space.
(Continues…)
Excerpted from "Introduction to Hilbert Space and the Theory of Spectral Multiplicity"
by .
Copyright © 2017 Paul R. Halmos.
Excerpted by permission of Dover Publications, Inc..
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Table of Contents
Preface
0. Prerequisites and Notation
CHAPTER I: The Geometry of Hilbert Space
1. Linear Functionals
2. Bilinear Functionals
3. Quadratic Forms
4. Inner Product and Norm
5. The Inequalities of Bessel and Schwarz
6. Hilbert Space
7. Infinite Sums
8. Conditions for Summability
9. Examples of Hilbert Spaces
10. Subspaces
11. Vectors in and out of Subspaces
12. Orthogonal Complements
13. Vector Sums
14. Bases
15. A Non-closed Vector Sum
16. Dimension
17. Boundedness
18. Bounded Bilinear Functionals
CHAPTER II: The Algebra of Operators
19. Operators
20. Examples of Operators
21. Inverses
22. Adjoints
23. Invariance
24. Hermitian Operators
25. Normal and Unitary Operators
26. Projections
27. Projections and Subspaces
28. Sums of Projections
29. Products and Differences of Projections
30. Infima and Suprema of Projections
31. The Spectrum of an Operator
32. Compactness of Spectra
33. Transforms of Spectra
34. The Spectrum of a Hermitian Operator
35. Spectral Heuristics
36. Spectral Measures
37. Spectral Integrals
38. Regular Spectral Measures
39. Real and Complex Spectral Measures
40. Complex Spectral Integrals
41. Description of the Spectral Subspaces
42. Characterization of the Spectral Subspaces
43. The Spectral Theorem for Hermitian Operators
44. The Spectral Theorem for Normal Operators
CHAPTER III: The Analysis of Spectral Measures
45. The Problem of Unitary Equivalence
46. Multiplicity Functions in Finite-dimensional Spaces
47. Measures
48. Boolean Operations on Measures
49. Multiplicity Functions
50. The Canonical Example of a Spectral Measure
51. Finite-dimensional Spectral Measures
52. Simple Finite-dimensional Spectral Measures
53. The Commutator of a Set of Projections
54. Pairs of Commutators
55. Columns
56. Rows
57. Cycles
58. Separable Projections
59. Characterizations of Rows
60. Cycles and Rows
61. The Existence of Rows
62. Orthogonal Systems
63. The Power of a Maximal Orthogonal System
64. Multiplicities
65. Measures from Vectors
66. Subspaces from Measures
67. The Multiplicity Function of a Spectral Measure
68. Conclusion
References
Bibliography
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