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Hilbert Space Geometry and Riesz Representation
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Areas of Elementary Plane Figures
- Banach Valued Integration and the Radon Nikodym Property
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Lp Spaces and Test-Function Conventions
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Density Separability and Convolution in Lᵖ
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces Adjoint Operators and Annihilators
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Fubini and Change of Variables
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Modes of Convergence Egorov and Lusin
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Norming and Separation under Hahn–Banach
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Simple Field Extensions and the Construction of the Complex Numbers
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Analytic Hahn Banach Theorem
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Weak Mixing and the Chacon Transformation
2 · Summary
The pair begins from the published definition of a real or complex inner product and the library's normed-space conventions, and it records the induced length as the published inner-product norm. Cauchy--Schwarz, the induced norm axioms, the parallelogram law and the Jordan--von Neumann polarisation characterisation are proved before any completeness is assumed, so the elementary geometry of the pairing is choice-free. Metric and product topology, the published real infimum machinery, the published completion of a normed space and the published countable-choice principle supply the analytic layer: the completion pairing is built from limits of pairings, and the closest-point theorem selects approximate minimisers, which is the exact place where Countable Choice is spent.
The page develops orthogonality, orthogonal complements, the closest point in a closed convex set with its variational characterisation, and the orthogonal decomposition of a Hilbert space by a closed subspace, from which the Hilbert orthogonal projection and its linearity, self-adjointness and contractivity follow. Riesz representation, the canonical reflexivity of Hilbert spaces, the Hilbert adjoint and its identities, the vocabulary of self-adjoint, positive, unitary and normal operators, and the kernel--range orthogonality of adjoints form the second half. The closing remarks record the agreement with the concrete projection construction and the ownership boundary that leaves Lax--Milgram to the partial-differential-equations track.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Real and complex inner-product spaces and their induced length
Definition
Let be or , with conjugation the identity on and complex conjugation on (Real and imaginary parts, complex conjugation, and modulus). A real or complex inner-product space is an -vector space together with an inner product in the sense of Real and complex inner product spaces, with the inner product linear in the first argument: for all and ,
Conjugate-linearity in the second argument. Conjugate symmetry together with linearity in the first argument gives, for all ,
Induced length. The induced length, or inner-product norm, of is
which is exactly the published The norm induced by a real or complex inner product: positive definiteness makes a nonnegative real with a unique nonnegative square root, so that , exactly for , and .
The convention on this page. Every inner-product space below is real or complex in this sense, the pairing is linear in the first argument and conjugate-linear in the second, and always denotes the induced length. In the real case conjugation is the identity and is the absolute value of ; in the complex case they are complex conjugation and the complex modulus. All statements below are therefore written once and read in either scalar field.
Cauchy–Schwarz: , with equality exactly for dependent pairs
Statement
For all vectors in a real or complex inner-product space,
with equality if and only if and are linearly dependent.
Facts & Assumptions
The pairing is linear in the first argument, conjugate-linear in the second, conjugate symmetric and positive definite, and the induced length satisfies with and exactly for (Real and complex inner-product spaces and their induced length).
The induced length is the unique nonnegative square root of the diagonal pairing, and positive definiteness makes the radicand a nonnegative real (The norm induced by a real or complex inner product).
Every nonnegative real has a unique nonnegative square root: there is exactly one with for each (Existence and uniqueness of -th roots: a unique with ).
For complex scalars , , and exactly for (Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive).
For real scalars , exactly for , and (Basic properties of the absolute value).
For nonnegative reals, if and only if (Squaring is monotone on the nonnegatives).
A finite list is linearly dependent when some choice of scalars, not all zero, makes the corresponding combination vanish; for the two-element list this means that for scalars not both zero (Linear independence: a finite list is independent when forces every , and a subset is independent when every injective finite list into is independent).
Proof
Given: Vectors in a real or complex inner-product space . In the real case read conjugation as the identity and as the absolute value of , so that [A4] is replaced by [A5].
If , then by conjugate-linearity in the second argument, and ; hence , while are dependent with witness scalars because and .
Suppose now and set , a well-defined scalar because ; expanding with linearity in the first argument, conjugate-linearity in the second and conjugate symmetry gives .
Multiplying step 1.2 by the positive number gives , and since , and are nonnegative, monotonicity of squaring on the nonnegatives gives the inequality .
If are dependent, then either , which is step 1.1, or for some scalar with ; in the second case and with both norms nonnegative, so by uniqueness of nonnegative square roots, and .
Conversely suppose and ; then and step 1.2 gives , so by positive definiteness, that is and the pair is dependent by [A7]; together with steps 1.1 and 2.2 this proves the inequality and both directions of the equality statement.
The induced length is a norm
Statement
Let be a real or complex inner-product space with induced length . Then with exactly for , for every scalar , and ; hence is a norm on , read over by A norm on a real vector space, the induced metric, and the dictionary with the metric axioms and over by Real and complex scalar conventions for normed spaces.
Facts & Assumptions
The induced length is the unique nonnegative square root of the diagonal pairing, with and exactly for (The norm induced by a real or complex inner product).
Cauchy–Schwarz gives (Cauchy–Schwarz: , with equality exactly for dependent pairs).
A norm on a real vector space satisfies separation, absolute homogeneity and the triangle inequality, and a complex normed space is defined by the same clauses with the complex modulus (A norm on a real vector space, the induced metric, and the dictionary with the metric axioms, Real and complex scalar conventions for normed spaces).
For complex scalars and (Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive), and for real scalars with (Basic properties of the absolute value).
For nonnegative reals if and only if (Squaring is monotone on the nonnegatives), and each nonnegative real has a unique nonnegative square root (Existence and uniqueness of -th roots: a unique with ).
If then and , so (Real and imaginary parts, complex conjugation, and modulus).
Proof
Given: A real or complex inner-product space , vectors and a scalar ; in the real case conjugation is the identity and is the absolute value of , so the second clause of [A4] is read in the real field.
Nonnegativity and separation are [A1]: , and exactly when , that is exactly when .
Homogeneity: by sesquilinearity and [A4], and both and are nonnegative with equal squares, so by uniqueness of the nonnegative square root.
Triangle inequality: expanding and using conjugate symmetry gives , and for every scalar , since either or with by [A6]; with [A2] this gives .
Both sides of the inequality in step 1.3 are nonnegative, so monotonicity of squaring on the nonnegatives turns it into .
Steps 1.1, 1.2 and 2.1 are exactly the clauses of [A3] over , and over they are the same clauses with the complex modulus, so is a norm on in either scalar field.
The parallelogram law
Statement
In every real or complex inner-product space, for all vectors ,
Facts & Assumptions
The pairing is linear in the first argument, conjugate-linear in the second and conjugate symmetric, and (Real and complex inner-product spaces and their induced length).
The induced length is the unique nonnegative square root of the diagonal pairing, so (The norm induced by a real or complex inner product).
Proof
Given: Vectors in a real or complex inner-product space.
Expanding with linearity in the first argument, conjugate-linearity in the second and conjugate symmetry gives and, replacing by , .
The cross terms in step 1.1 cancel when the two expansions are added, so , the parallelogram law.
Jordan–von Neumann: a norm is induced by an inner product exactly when it satisfies the parallelogram law
Statement
Let be a real or complex vector space with a norm . Then is induced by an inner product on if and only if it satisfies the parallelogram law
In that case the inner product is unique, and it is given by the real polarisation formula
in the real case, and by the complex polarisation formula
in the complex case with the first-variable-linear convention.
Facts & Assumptions
In a real or complex inner-product space the pairing is linear in the first argument, conjugate-linear in the second, conjugate symmetric and positive definite, and (Real and complex inner-product spaces and their induced length).
The induced length is the unique nonnegative square root of the diagonal pairing (The norm induced by a real or complex inner product).
A norm satisfies for , in particular , and in the complex case, and it satisfies the triangle inequality; complex normed spaces follow the scalar convention of the remark (A norm on a real vector space, the induced metric, and the dictionary with the metric axioms, Real and complex scalar conventions for normed spaces).
Every real or complex inner-product norm satisfies the parallelogram law (The parallelogram law).
Every real number is approximated by rationals: for and rational there is a rational with (The rationals embed densely in the reals).
For every real there is a natural with (For every in a complete ordered field there is a natural with ).
For complex scalars , , and (Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive).
For nonnegative reals if and only if ; every nonnegative real has a unique nonnegative square root (Squaring is monotone on the nonnegatives, Square roots exist: a unique with ; the positives are ).
Proof
Given: A real or complex vector space with a norm ; write . The real case is proved first, then the complex case, and finally necessity.
Assume first that is real and satisfies the parallelogram law, and define ; then , , , , and for real .
Applying the parallelogram law to the four pairs , , and and subtracting the fourth identity from the third gives , that is for all .
Adding that identity at and at gives and ; since by step 1.1, the two relations add to , so , and symmetry gives additivity in the second argument as well.
Induction on the natural number using step 2.1 gives , and is step 1.1, so for every integer .
For the additivity of step 2.1 gives , so , and together with step 3.1 this yields for every rational .
Consequently for every rational the form is a symmetric rational-bilinear pairing with , that is by steps 1.1 and 4.1.
If , put and , so that step 5.1 reads for every rational ; rationals approach within any by [A5], whence for every , and because a negative would give for some by [A6]; if instead then for all rational forces , since otherwise [A6] supplies a rational with ; in both cases , so by [A8].
For fixed the map is additive in by step 2.1 and satisfies by step 6.1, hence for ; given choose with by [A6], then gives , so is continuous at .
For real and rational one has with by step 4.1, so continuity at from step 7.1 and the rational approximation of from [A5] give , that is for every real .
Therefore, in the real case, is symmetric, additive in each argument and real-homogeneous in the first argument, with and exactly for ; so is a real inner product on whose induced length is the given norm .
Now let be complex with a norm satisfying the parallelogram law; viewing as a real vector space with the same norm, to which step 9.1 applies, gives a real inner product with , and together with the definition of gives , hence by the argument .
Define ; then additivity in both arguments and follow from the real bilinearity of , and conjugate symmetry follows from and from in step 10.1.
Positivity: because and force ; hence is a complex inner product whose induced length is , and expanding in terms of gives the complex polarisation formula .
Conversely, if the given norm is induced by an inner product on , then it satisfies the parallelogram law by [A4] and expanding the pairing in terms of recovers, in the real case, and, in the complex case, the four-term formula of step 12.1; with steps 9.1 and 12.1 this proves that a real or complex norm is induced by an inner product exactly when it satisfies the parallelogram law, and that the polarisation formulas display that inner product.
Hilbert space
Definition
A real Hilbert space is a real inner-product space (Real and complex inner-product spaces and their induced length) whose induced-length metric is complete in the sense of Complete metric space: every Cauchy sequence converges in the space: every Cauchy sequence in for the norm (The induced length is a norm) converges to a point of . A complex Hilbert space is a complex inner-product space with the same completeness property, so that a Hilbert space is exactly a real or complex inner-product space that is a Banach space for its induced norm (Banach space).
The completion convention is the Cauchy-sequence one. Banach space defines completeness by convergence of Cauchy sequences, and this page keeps that convention throughout. It is weaker than -completeness — the assertion that every decreasing sequence of nonempty closed subsets with diameters tending to zero has a nonempty intersection. The two agree in ZFC, but Blackadar, Farah and Karagila note that the closest-point theorem on a -complete inner-product space is provable in ZF, whereas the Cauchy-complete form used below consumes the Axiom of Countable Choice; nothing here silently imports the stronger notion.
The inner product is jointly continuous
Statement
Let be a real or complex inner-product space with induced norm . The pairing is continuous on for the product of the induced norm topologies. Quantitatively, for all ,
and consequently and in norm imply .
Facts & Assumptions
The pairing is linear in the first argument and conjugate-linear in the second (Real and complex inner-product spaces and their induced length).
Cauchy–Schwarz gives (Cauchy–Schwarz: , with equality exactly for dependent pairs).
The induced length is a norm, so it is nonnegative, homogeneous and satisfies the triangle inequality (The induced length is a norm).
In the metric topology a set is open exactly when every one of its points has a ball around it inside the set, is the open ball, and every open ball is open (The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement, Open ball, closed ball and sphere in a metric space, Arbitrary unions and finite intersections of open sets are open, open balls are open and closed balls are closed).
For a finite product the boxes with open are basic product-open sets (The product set of functions choosing a point in each factor, the projections, the box topology, and the product topology as the initial topology of the projections; the empty product is a one-point space).
Convergence in a metric space means that the distances to the limit tend to zero (Convergence of a sequence in a metric space: iff in ).
Proof
Given: A real or complex inner-product space , vectors and a point of continuity of .
Inserting and subtracting the mixed pairing gives , so Cauchy–Schwarz applied to the two summands and the triangle inequality for scalars give .
Given , put ; if and , then by the triangle inequality, so step 1.1 gives .
The product is a basic product-open set containing , and step 2.1 shows that on it the pairing stays within every ball about , so the pairing is continuous at every point of ; if moreover and , then for every the pair eventually lies in the corresponding -box, whence and .
The norm completion of an inner-product space is a Hilbert space
Statement
Assume the Axiom of Countable Choice. Let be a real or complex inner-product space and let be its norm completion (Completion of a normed space). Then carries a unique inner product that extends the given one along and whose induced length is the completion norm; with it is a Hilbert space, and the extension is unique among inner products that extend the given pairing and induce the completion norm.
Facts & Assumptions
The completion is a Banach space, is a dense linear isometry, and carries the unique compatible vector-space structure of the published metric completion (The metric completion of a normed space carries a unique compatible Banach-space structure, Completion of a normed space).
The induced length of an inner-product space is a norm (The induced length is a norm). Conversely, a norm on a real or complex vector space is induced by an inner product if and only if it satisfies the parallelogram law. That inner product is unique and is recovered by the real or first-linear complex polarisation formula (Jordan–von Neumann: a norm is induced by an inner product exactly when it satisfies the parallelogram law).
Cauchy–Schwarz holds in every inner-product space; the resulting two-variable difference estimate proves joint continuity (Cauchy–Schwarz: , with equality exactly for dependent pairs, The inner product is jointly continuous).
Vector addition and scalar multiplication are continuous, and (Vector addition and scalar multiplication are continuous in a normed space, The reverse triangle inequality in a normed space).
Countable Choice selects a point from every member of a countable family of nonempty sets, and (The Axiom of Countable Choice (), For every in a complete ordered field there is a natural with ).
Every real or complex inner-product norm satisfies the parallelogram law (The parallelogram law).
A Hilbert space is an inner-product space complete for its induced-norm metric (Hilbert space).
Proof
Given: Countable Choice, an inner-product space with norm , and its norm completion with completion norm also written .
Let . For every , density makes the sets and nonempty. Countable Choice selects from these sets. Thus and , the selected sequences are Cauchy, and continuity of the vector operations gives .
The parallelogram identities hold in by [A6]; passing to the limit using continuity of the norm and of sums, and the isometry , gives , so the completion norm satisfies the parallelogram law.
The Jordan–von Neumann theorem applied to the Banach space therefore produces an inner product on whose induced length is exactly the completion norm and which is the unique such inner product.
extends the original pairing: for , the polarisation formula of [A2] together with and the isometry property gives in the real case, and the four-term complex formula likewise in the complex case.
Uniqueness: if is any inner product on that extends the pairing along and induces the completion norm, then for and approximating sequences as in step 1.1 the Cauchy–Schwarz inequality for both forms gives and the same bound for , so both pairings are the limits of the common values and .
Hence carries the inner product extending the original one with the completion norm as induced length, so it is complete and therefore a Hilbert space by [A7]. Countable Choice is used in the published completion interface and explicitly in step 1.1 to select the two approximating sequences; no further choice enters the limit arguments.
Orthogonality and the orthogonal complement
Definition
Let be a real or complex inner-product space. Vectors are orthogonal, written , when
For a subset the orthogonal complement of is
Orthogonality is symmetric. If , then , so exactly when ; in particular the condition defining is symmetric in its two arguments.
is a linear subspace. Let and scalars ; then , and if then by linearity in the first argument, so . Thus is a linear subspace of (Linear subspace of a vector space) for every subset , whether or not is a subspace. Moreover always, and lies in for every , so .
Monotonicity. If , then every vector orthogonal to all of is orthogonal to all of , so .
Nontriviality of orthogonality. By positive definiteness exactly for (The induced length is a norm), so a vector orthogonal to itself is zero, and , .
Pythagoras and finite orthogonal sums
Statement
Let be pairwise orthogonal vectors in a real or complex inner-product space, that is whenever (Orthogonality and the orthogonal complement). Then
the empty sum on the right being at .
Facts & Assumptions
Orthogonality means , the pairing is linear in the first argument and conjugate-linear in the second, and (Orthogonality and the orthogonal complement, Real and complex inner-product spaces and their induced length).
Every inner-product norm satisfies the parallelogram law (The parallelogram law).
Proof
Given: Pairwise orthogonal vectors in a real or complex inner-product space.
At the sum is and both sides vanish, and at the identity is .
For two orthogonal vectors , expansion gives , and the same computation with replaced by shows that the sum of a finite orthogonal family may be split off one term at a time.
Suppose the identity holds for orthogonal families of terms, , and let be pairwise orthogonal; the partial sum satisfies by linearity in the first argument, and by the induction hypothesis, so .
Induction on from the cases of step 1.1 and the induction step of step 2.1 proves the identity for every , so pairwise orthogonal vectors satisfy .
Orthogonal complements are closed
Statement
For every subset of a real or complex inner-product space , the orthogonal complement is a closed linear subspace of for the induced norm topology.
Facts & Assumptions
is a linear subspace and orthogonality is symmetric (Orthogonality and the orthogonal complement).
Cauchy–Schwarz gives (Cauchy–Schwarz: , with equality exactly for dependent pairs).
The induced length is a norm, so with exactly for (The induced length is a norm).
In the metric topology a set is open exactly when every point of it has a ball around it inside the set, and (The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement, Open ball, closed ball and sphere in a metric space).
Proof
Given: A subset of a real or complex inner-product space , with as in [A1].
By [A1] the set is a linear subspace of , which is the first assertion.
Let ; then some has , so and ; if satisfies , then by Cauchy–Schwarz, so .
Thus every point outside has a ball around it that misses , so is open and is closed in the metric topology; with step 1.1 this proves that is a closed linear subspace.
A minimizing sequence in a convex set is Cauchy
Statement
Let be a nonempty convex subset of a real or complex inner-product space, let be a vector and put . If is a sequence with , then is a Cauchy sequence.
Facts & Assumptions
A subset is convex when for all and (Convex sets and continuous real-hyperplane separation in a normed space).
If then for every (Greatest lower bound (infimum)).
Every inner-product norm satisfies the parallelogram law (The parallelogram law).
The norm is induced by the pairing, in particular is the distance between and and (Real and complex inner-product spaces and their induced length).
Proof
Given: A nonempty convex set , a vector , the number and a sequence with .
Put ; for all the midpoint lies in by convexity, so because is a lower bound of the distances from to points of .
The parallelogram law applied to gives .
Given , convergence provides with for all , and then step 2.1 gives for all ; hence is Cauchy.
Projection onto a nonempty closed convex set
Statement
Assume the Axiom of Countable Choice. Let be a real or complex Hilbert space, let be nonempty, closed and convex, and let . Then there is exactly one point with , that is, a unique nearest point of to .
Facts & Assumptions
A Hilbert space is an inner-product space complete for its induced norm: every Cauchy sequence converges to a point of the space (Hilbert space).
Every nonempty real set bounded below has an infimum, characterised by points arbitrarily close from above (Every nonempty set bounded below has an infimum, Epsilon characterisation of the infimum).
Countable Choice selects a point from each set of a countable family of nonempty sets (The Axiom of Countable Choice ()).
A minimizing sequence in a nonempty convex set is Cauchy (A minimizing sequence in a convex set is Cauchy).
is convex and for every (Convex sets and continuous real-hyperplane separation in a normed space, Greatest lower bound (infimum)). A closed set equals its closure, and a point lies in the closure whenever every ball about it meets the set (The closure of a nonempty is , equals together with its limit points, and is the smallest closed superset); limits in a metric space are unique (A sequence in a metric space has at most one limit).
Norm distance is continuous: (The reverse triangle inequality in a normed space).
Convergence of a sequence of reals to means that for every the terms are eventually within of , and for every some is below (Limits and Cauchy sequences of reals, For every in a complete ordered field there is a natural with ).
Every inner-product norm satisfies the parallelogram law (The parallelogram law).
Proof
Given: Countable Choice, a real or complex Hilbert space , a nonempty closed convex set and a vector .
The set is nonempty and bounded below by , so exists and for every some has by the epsilon characterisation of the infimum.
Countable Choice selects for every a point with , the sets being nonempty by step 1.1.
Then for every , so by the Archimedean reciprocal bound; hence is Cauchy by the minimizing-sequence lemma, completeness of gives a limit , and closedness of places in .
Moreover : norm continuity along the limit gives , and a limit of the sequence is unique.
If both satisfy , then the midpoint lies in by convexity, so , and the parallelogram law gives , whence : the nearest point is unique.
Variational characterisation of the nearest point
Statement
Assume the Axiom of Countable Choice. Let be a real or complex Hilbert space, let be closed and convex with , and let . Then is the nearest point of to if and only if
In particular, for the nearest point the inequality holds, and conversely any satisfying the inequality is nearest.
Facts & Assumptions
is convex: for and ; and is nearest exactly when for every (Convex sets and continuous real-hyperplane separation in a normed space).
The pairing is linear in the first argument and conjugate-linear in the second, with (Real and complex inner-product spaces and their induced length).
Countable Choice is the selection principle consumed by the existence theorem for nearest points (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a closed convex set , a vector and a point .
Suppose first that is nearest, and let ; for every with the convexity hypothesis gives , so , whence , and if were positive the choice would make the right-hand side strictly smaller than the left, a contradiction; hence .
Conversely suppose for every ; then , because .
Steps 1.1 and 1.2 prove both implications of the stated equivalence; the Countable Choice hypothesis is used only to invoke the existence theorem for nearest points (Projection onto a nonempty closed convex set) when the nearest point is not supplied, while the equivalence itself is choice-free for a given .
Orthogonal decomposition by a closed subspace
Statement
Assume the Axiom of Countable Choice. Let be a closed linear subspace of a real or complex Hilbert space . Then every has a unique decomposition
so that as a direct sum of the subspace and its orthogonal complement.
Facts & Assumptions
A linear subspace is convex and contains , and the nearest point of a nonempty closed convex subset of a Hilbert space exists and is unique (Linear subspace of a vector space, Projection onto a nonempty closed convex set).
A point is the nearest point of a closed convex set to exactly when for every (Variational characterisation of the nearest point).
The pairing is linear in the first argument and conjugate-linear in the second, is a linear subspace, and with forces (Orthogonality and the orthogonal complement, Real and complex inner-product spaces and their induced length).
Countable Choice is the selection principle consumed by the nearest-point theorem (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a Hilbert space , a closed linear subspace and a vector .
Since is a nonempty closed convex set, has a unique nearest point in .
The variational inequality gives for every . For , take and to obtain . Over the pairing is real-valued, so this already gives . Over , also , and the same real-part conclusion applied to gives . Thus in either scalar field for every , that is .
Setting and gives a decomposition with and .
If are two such decompositions, then lies in , since both and are linear subspaces, so and ; hence , , and the decomposition is unique.
The Hilbert orthogonal projection onto a closed subspace
Definition
Assume the Axiom of Countable Choice. Let be a closed linear subspace of a real or complex Hilbert space (Linear subspace of a vector space). By the orthogonal-decomposition theorem (Orthogonal decomposition by a closed subspace) every has a unique representation
and the Hilbert orthogonal projection onto is the map
assigning to its unique -component. Its defining properties are therefore
which characterise uniquely: a map with these defining properties must agree with the -component of the unique decomposition of each .
Agreement with the finite-dimensional projection. If is a finite-dimensional inner-product space and a subspace, then For a subspace of a finite-dimensional inner product space, writes and The orthogonal projection is the -component in defines as the unique -component of ; the defining properties displayed above are the same, so they define the same map on a finite-dimensional Hilbert space.
Range and kernel. for every , and for because with ; conversely says exactly that . Hence the range of is and its kernel is , and is the identity on and zero on .
Hilbert projections are linear, self-adjoint and contractive
Statement
Assume the Axiom of Countable Choice. Let be a closed linear subspace of a real or complex Hilbert space and let be the Hilbert orthogonal projection onto . Then:
- is linear and idempotent, and ;
- for all , that is is self-adjoint;
- for every , so is a bounded linear operator of norm at most ; and if , then .
Facts & Assumptions
and , and a vector in is orthogonal to every vector of (The Hilbert orthogonal projection onto a closed subspace, Orthogonality and the orthogonal complement).
and are linear subspaces, so they are closed under sums and scalar multiples (Linear subspace of a vector space, Orthogonality and the orthogonal complement).
For pairwise orthogonal vectors, (Pythagoras and finite orthogonal sums).
A bounded linear operator has finite operator norm, and with (A bounded linear operator between normed spaces, The operator norm as the least bound and as the unit-sphere or unit-ball supremum).
Countable Choice is the assumption under which the projection is defined (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a closed linear subspace of a Hilbert space and the projection .
Linearity: for scalars and , the vector lies in and lies in , so by the defining property of it equals ; idempotence follows because has zero orthogonal component, so .
Range and kernel: always, for because , and exactly when ; hence and .
Self-adjointness: writing and using additivity in the second argument together with and gives , and symmetrically ; hence the two pairings agree.
Contractivity: is a sum of orthogonal vectors, so Pythagoras gives , hence and by the definition of the operator norm; if choose , then gives , so .
The double orthogonal complement of a subspace is its closure
Statement
Assume the Axiom of Countable Choice. Let be a linear subspace of a real or complex Hilbert space . Then
where is the norm closure of and .
Facts & Assumptions
is a linear subspace, , and implies (Orthogonality and the orthogonal complement).
is closed for every subset , and the closure of a set is the smallest closed superset, so is contained in every closed set containing . A point lies in exactly when every norm ball about it meets (Orthogonal complements are closed, The closure of a nonempty is , equals together with its limit points, and is the smallest closed superset).
is a linear subspace and (Orthogonality and the orthogonal complement).
The inner-product norm is homogeneous and satisfies the triangle inequality (The induced length is a norm).
Every closed linear subspace of splits as (Orthogonal decomposition by a closed subspace).
Countable Choice is the hypothesis under which the decomposition is available (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a Hilbert space and a linear subspace .
Every is orthogonal to every element of , so ; since is closed by [A2], and is the smallest closed superset of , we get .
The closure is a linear subspace. It contains . If and , choose with and ; then and , so every ball about meets and . If , then ; if , for every choose with , and then and , so .
Let and decompose with and ; then lies in because both and do, while because ; hence , so and .
Therefore by step 2.1, and the reverse inclusion is step 1.1, so for every linear subspace .
Riesz representation for Hilbert spaces
Statement
Assume the Axiom of Countable Choice. Let be a real or complex Hilbert space and let be a bounded linear functional on (The dual space X^* of a normed space and its dual norm). Then there is a unique with
and , where is the dual norm (The operator norm as the least bound and as the unit-sphere or unit-ball supremum). Under the first-variable-linear convention the representing vector depends conjugate-linearly and isometrically on : if is represented by and are scalars, then is represented by .
Facts & Assumptions
If is a bounded linear functional then and , and exactly when (The operator norm as the least bound and as the unit-sphere or unit-ball supremum, A bounded linear operator between normed spaces).
Cauchy–Schwarz gives , and the pairing is linear in the first argument and conjugate-linear in the second with (Cauchy–Schwarz: , with equality exactly for dependent pairs, Real and complex inner-product spaces and their induced length).
The kernel of a bounded linear functional is either all of or a proper linear subspace, and the orthogonal complement of a subspace is closed under the decomposition for closed (Linear subspace of a vector space, Orthogonal decomposition by a closed subspace).
For a subset , (Orthogonality and the orthogonal complement).
Countable Choice is the hypothesis under which the orthogonal decomposition, and hence this representation, is obtained (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a real or complex Hilbert space and a bounded linear functional on .
If , then represents because for every , and ; the same has no other representing vector, since a vector representing satisfies and hence .
If , its kernel is a proper linear subspace of : it is linear because , it is proper because takes a nonzero value, and it is closed because and give .
Choose with and put , so ; decompose with and , then shows and .
For arbitrary , the vector satisfies , hence and ; moreover , so with .
Norm and uniqueness: Cauchy–Schwarz gives , so , while gives ; hence , and this contains the case . If also for all , then for all , and the choice gives , so .
Conjugate linearity: if for , then for all and scalars one has by conjugate-linearity in the second argument; uniqueness of the representing vector therefore gives the representing vector , so the map is conjugate-linear, and by step 4.1 it is isometric.
Steps 1.1, 3.1 and 4.1 produce the unique representing vector together with the norm identity for every bounded , and step 5.1 records its conjugate-linear isometric dependence; Countable Choice is used exactly through the orthogonal decomposition of step 2.1.
Hilbert spaces are reflexive
Statement
Assume the Axiom of Countable Choice. Every real or complex Hilbert space is reflexive: the canonical evaluation map is surjective.
Facts & Assumptions
Riesz representation: for every bounded linear functional on there is a unique with for all , and ; writing defines a bijection that is conjugate-linear, and linear in the real case (Riesz representation for Hilbert spaces, The dual space X^* of a normed space and its dual norm).
The canonical map is and does not depend on choices (The canonical evaluation map into the bidual).
is reflexive exactly when is surjective (Reflexivity is surjectivity of the canonical map).
The pairing is conjugate-linear in the second argument, so (Real and complex inner-product spaces and their induced length).
Countable Choice is the hypothesis of the Riesz representation theorem used below (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, a real or complex Hilbert space , its dual , bidual and canonical map .
By [A1] the Riesz map is a bijection with and .
Let and define ; since is conjugate-linear and is linear, and , while ; hence is a linear functional on with .
Applying Riesz representation to gives with for every .
Then for every one has by [A4] and [A2]; since is onto , every element of has the form , so lies in the range of .
Thus is surjective and is reflexive; the only choice assumption is the one inherited from Riesz representation in step 1.1.
The Hilbert-space adjoint of a bounded operator
Definition
Assume the Axiom of Countable Choice. Let and be real or complex Hilbert spaces and let be a bounded linear operator (A bounded linear operator between normed spaces, The spaces (\mathcal B(X,Y)) and (\mathcal B(X)) of bounded linear operators). For fixed the map
is a bounded linear functional on : it is linear in because is linear and the pairing is linear in its first argument, and by Cauchy–Schwarz and the operator-norm inequality (Cauchy–Schwarz: , with equality exactly for dependent pairs, The operator norm as the least bound and as the unit-sphere or unit-ball supremum). By Riesz representation (Riesz representation for Hilbert spaces) there is therefore a unique vector with
The Hilbert adjoint of is the map
It is the unique map satisfying the displayed identity, since two such maps have for all and hence by positive definiteness.
The dictionary with the Banach transpose. Write and for the Riesz maps and , and let be the transpose of , (The transpose of a bounded operator). Then for all and ,
so . The Hilbert adjoint is thus the Banach transpose conjugated by the Riesz identifications of and with their duals; it is a different operator from whenever the Riesz maps are conjugate-linear.
Hilbert-adjoint identities
Statement
Assume the Axiom of Countable Choice. Let be real or complex Hilbert spaces and let and be bounded linear operators. Then the Hilbert adjoints satisfy:
- for scalars , and the adjoint of an operator is unique;
- ;
- and ;
- .
Facts & Assumptions
The Hilbert adjoint of is the unique map with for all (The Hilbert-space adjoint of a bounded operator).
The pairing is linear in the first argument, conjugate-linear in the second, conjugate symmetric, and implies (Real and complex inner-product spaces and their induced length).
The operator norm satisfies and is the unit-ball supremum (The operator norm as the least bound and as the unit-sphere or unit-ball supremum), while composition obeys (Composition satisfies |ST|\le|S|,|T|); a linear map is bounded if it admits a finite constant with for every (A bounded linear operator between normed spaces).
Cauchy–Schwarz gives (Cauchy–Schwarz: , with equality exactly for dependent pairs).
Countable Choice is the hypothesis of the Riesz construction of adjoints (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, Hilbert spaces and bounded operators , .
First let be any bounded linear operator between Hilbert spaces over the same scalar field. The map exists by the adjoint construction. For , scalars and , its identity gives . Positive definiteness applied to the difference proves that is linear. Also , so division when , and the trivial inequality otherwise, give . Thus is bounded and its now-defined operator norm satisfies . This applies to each bounded operator used below, including an adjoint once its boundedness has been established.
For uniqueness of the adjoint and conjugate-linearity in the operator, let both satisfy the defining adjoint identity for the same operator in . For , one has for every ; taking gives . For scalars , the identities hold for all and , so .
Composition and involution: for and , , so by uniqueness. Likewise, for and , the defining identity for gives ; conjugate symmetry and the defining identity for give , so by uniqueness.
Norms: Cauchy–Schwarz gives , hence (trivially when ) and ; applying this to and using gives . Moreover , while for one has , so and hence .
Self-adjoint, positive, unitary and normal operators
Definition
Assume the Axiom of Countable Choice, and let be a real or complex Hilbert space with a bounded linear operator and its Hilbert adjoint (The Hilbert-space adjoint of a bounded operator, The spaces (\mathcal B(X,Y)) and (\mathcal B(X)) of bounded linear operators).
- is self-adjoint when ;
- is positive when is a real number in for every ;
- is unitary when , the identity operator;
- is normal when .
The definitions are read over either scalar field with the same inner product. Two immediate consequences. Every self-adjoint operator is normal, because when . Every unitary operator is normal, because its defining identity says exactly that and are both the identity. Positivity is a condition on the values of the quadratic form and therefore forces those values to be real, which for a complex Hilbert space does not follow from boundedness alone; a positive operator on a complex Hilbert space is in fact self-adjoint, but that is proved later and is not assumed here.
Consistency of the vocabulary. The identity operator is self-adjoint, positive and unitary, and the zero operator is self-adjoint and positive; on the one-dimensional Hilbert space the operator is self-adjoint exactly when is real, unitary exactly when , and normal for every scalar .
Kernel–range orthogonality for Hilbert adjoints
Statement
Assume the Axiom of Countable Choice. Let be a bounded linear operator between real or complex Hilbert spaces, with kernel , range and Hilbert adjoint . Then
and consequently
Facts & Assumptions
for all , , and the adjoint is a bounded linear operator (The Hilbert-space adjoint of a bounded operator, A bounded linear operator between normed spaces).
; if for every then , and for every linear subspace (Orthogonality and the orthogonal complement, Real and complex inner-product spaces and their induced length, The double orthogonal complement of a subspace is its closure).
The image of a linear map is a linear subspace, and the kernel of a linear map is a linear subspace (Linear subspace of a vector space).
Countable Choice is the hypothesis under which adjoints and double complements are available (The Axiom of Countable Choice ()).
Proof
Given: Countable Choice, Hilbert spaces and a bounded linear operator .
For , one has exactly when for every , which by the adjoint identity is exactly when for every ; taking shows this happens exactly when , that is .
Replacing by in step 1.1 and using gives .
The range of a linear map is a linear subspace, so the double-complement theorem applies to it: , and likewise .
Agreement with the concrete L-two projection
Remark
Assume the Axiom of Choice, and let be complex or a closed linear subspace of it with a closed linear subspace (Closed l two subspaces have orthogonal projections). That published theorem constructs a linear contraction with and for every , together with .
The Hilbert orthogonal projection of The Hilbert orthogonal projection onto a closed subspace is characterised by the same two properties: its value at is the unique -component of the unique orthogonal decomposition with . Since the concrete construction supplies a vector of whose residual is orthogonal to , and the orthogonal decomposition is unique, the concrete map and the Hilbert projection agree wherever both are defined. The concrete theorem is used only to identify the two constructions: it is not a supplier for the existence, linearity, contractivity or self-adjointness of the Hilbert projection, which are proved on this page from the abstract decomposition.
The choice cost of the identification is the concrete theorem's: it assumes AC, and AC implies the Axiom of Countable Choice, under which the abstract projection exists (The Axiom of Choice). No stronger principle is claimed, and the identification is orientation only, not a load-bearing prerequisite of any theorem on this page.
Lax–Milgram is owned by the PDE track
Remark
The seam contract of this track assigns the Lax–Milgram theorem and the vocabulary of bounded and coercive sesquilinear forms to the partial-differential-equations development, and this page does not state them. What this page supplies is Riesz representation for Hilbert spaces, which the PDE track may cite, together with the bounded-operator vocabulary of A bounded linear operator between normed spaces: on a real or complex Hilbert space every bounded linear functional is represented by a vector, and that is the ingredient from which a later page proves Lax–Milgram after defining its own forms, boundedness, coercivity and continuity hypotheses.
Nothing on the present page assumes coercivity, symmetry or continuity of a sesquilinear form, and no representation of a form by an operator is asserted. Assuming the Axiom of Countable Choice, the Riesz theorem available here carries that hypothesis, and any later consumer that invokes it inherits the same assumption (The Axiom of Countable Choice ()).
5 · Examples, counterexamples and false statements
None yet.
Sources
- Theo Bühler and Dietmar Salamon, Functional Analysis, §1.3.3, §2.3.6 and §§5.3.1–5.3.2
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Lectures 15–16 and 22–23
- Theo Bühler and Dietmar Salamon, Functional Analysis, §1.3.3, Lemma 1.40, p.38
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Lectures 15–16
- Theo Bühler and Dietmar Salamon, Functional Analysis, §1.3.3, pp.38–39
- Theo Bühler and Dietmar Salamon, Functional Analysis, §1.3.3, p.39
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Lecture 15
- Theo Bühler and Dietmar Salamon, Functional Analysis, Definition 1.41, p.39
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Lecture 16
- Bruce Blackadar, Ilijas Farah and Asaf Karagila, Hilbert spaces without the Countable Axiom of Choice, Definitions 1.0.1 and 2.0.1
- Theo Bühler and Dietmar Salamon, Functional Analysis, §1.3.3, p.38
- Theo Bühler and Dietmar Salamon, Functional Analysis, Theorem 1.44, pp.40–41
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 178
- Bruce Blackadar, Ilijas Farah and Asaf Karagila, Hilbert spaces without the Countable Axiom of Choice, Theorem 2.0.4
- Theo Bühler and Dietmar Salamon, Functional Analysis, Lemma 5.37, p.238
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 180
- Bruce Blackadar, Ilijas Farah and Asaf Karagila, Hilbert spaces without the Countable Axiom of Choice, Corollary 2.0.5
- Theo Bühler and Dietmar Salamon, Functional Analysis, Definition 5.36, p.237
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Definition 182 and Proposition 183
- Theo Bühler and Dietmar Salamon, Functional Analysis, Definition 5.36 and Lemma 5.38, pp.237–238
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Proposition 183
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 181
- Theo Bühler and Dietmar Salamon, Functional Analysis, Theorems 1.43 and 5.35, pp.39 and 236
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 184
- Bruce Blackadar, Ilijas Farah and Asaf Karagila, Hilbert spaces without the Countable Axiom of Choice, Theorem 2.0.6
- Theo Bühler and Dietmar Salamon, Functional Analysis, Theorem 5.35, p.236
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Theorem 185
- Theo Bühler and Dietmar Salamon, Functional Analysis, Definition 5.39, p.239
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis, Lecture 23
- Theo Bühler and Dietmar Salamon, Functional Analysis, Lemma 5.38, p.238