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Inner Product Spaces and Orthogonality: Examples and Counterexamples
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Determinants of Matrices over a Commutative Ring
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Simple Field Extensions and the Construction of the Complex Numbers
- Suprema and Infima
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The ZFC Axioms and the Basic Set Constructions
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Positive coordinate weights define an inner product and change lengths and projections
Example
For positive real weights ,
is an inner product on , where or . With weights on , the vector has length , and the projection of onto is rather than the standard projection .
Facts & Assumptions
Given: Positive weights and the displayed pairing.
A real or complex inner product is linear in the first argument, conjugate symmetric, positive on the diagonal, and definite (Real and complex inner product spaces, with the inner product linear in the first argument).
The induced norm is (The norm induced by a real or complex inner product).
For an orthonormal basis of a subspace of a finite-dimensional inner product space, (Orthogonal projection is linear, and an orthonormal basis of gives ).
The empty finite sum has additive value zero (The product of a finite list in a monoid, by recursion, with the empty product () equal to the identity).
Verification
Finite-sum algebra gives linearity, conjugate symmetry, and . Because every , this sum vanishes exactly when every coordinate of vanishes, so [L1] holds. For , [L4] leaves only the zero vector.
For weights , [L2] gives . Put , and . Then is an orthonormal basis of , so [L3] gives . Here and , so . With both weights the same computation uses and and gives .
The Frobenius inner product on a real or complex matrix space
Example
On , where or , the formula
defines the Frobenius inner product, and
Facts & Assumptions
Given: Matrices of one fixed shape.
Matrices of a fixed shape form a vector space under entrywise operations (The vector space of by matrices over a field, with entrywise operations).
An inner product must be linear in its first argument, conjugate symmetric, positive, and definite (Real and complex inner product spaces, with the inner product linear in the first argument).
Complex conjugation distributes over finite sums and satisfies , vanishing exactly at (Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive).
Empty finite sums have additive value zero (The product of a finite list in a monoid, by recursion, with the empty product () equal to the identity).
Verification
Entrywise operations and finite-sum algebra give linearity in . Applying conjugation termwise and using [L3] gives conjugate symmetry.
On the diagonal, [L3] gives the displayed sum of squared moduli. It is nonnegative and vanishes exactly when every entry vanishes, which by [L1] means . Hence [L2] is satisfied.
If or , the matrix space contains only its zero matrix and [L4] makes the formula zero, so definiteness remains valid.
Evaluation at four distinct real points gives an inner product on polynomials of degree at most three
Example
On the real vector space of polynomials of degree at most , evaluation at four distinct real points defines an inner product by
For example, the points may be .
Facts & Assumptions
Given: Four distinct real scalars and polynomials .
An inner product is a symmetric bilinear form whose diagonal is nonnegative and vanishes only at zero (Real and complex inner product spaces, with the inner product linear in the first argument).
A nonzero polynomial over a domain has at most as many distinct roots as its degree (A nonzero polynomial of degree over an integral domain has at most distinct roots).
Finite sums are compatible with the additive and multiplicative laws of the scalar field (The product of a finite list in a monoid, by recursion, with the empty product () equal to the identity).
Verification
By [L3], the displayed formula is bilinear and symmetric. Also .
If , every real square in the sum is zero, so for four distinct . If were nonzero, [L2] would give at most three roots because . Thus , proving definiteness and hence [L1].
Gram–Schmidt on an explicit basis of
Example
Applying Gram–Schmidt in standard to
produces
Facts & Assumptions
Given: The displayed ordered basis in standard .
The standard coordinate inner product is the sum of coordinatewise products (The standard formulas on and on are inner products).
Gram–Schmidt subtracts earlier orthogonal projections, normalises the nonzero residual, and preserves each successive span (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).
Verification
By [L1], , giving . The second residual is , whose norm is , giving .
The third residual is . Its norm is , so normalisation gives the displayed .
Direct dot products give . Moreover is a nonzero multiple of , , and differs from by a combination of the first two residual directions, verifying all three successive-span equalities from [L2].
Gram–Schmidt on an explicit basis of with conjugation visible
Example
For the linear-first standard inner product on , Gram–Schmidt applied to and produces
Facts & Assumptions
Given: The vectors and in .
The standard complex inner product is (The standard formulas on and on are inner products).
Gram–Schmidt uses the coefficient under the linear-first convention (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).
Complex conjugation sends to and satisfies (Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive).
Verification
From [L1], , so . The projection coefficient is .
Subtraction gives . By [L3], , so .
Direct use of [L1] gives and, by conjugate symmetry, . Both vectors have norm one, so the displayed list is orthonormal.
A worked QR factorisation of an invertible real matrix
Example
The invertible matrix
has the QR factorisation
Facts & Assumptions
Given: The displayed real matrix .
QR factorisation applies Gram–Schmidt to the columns and requires an upper-triangular factor with positive diagonal (Every invertible real or complex square matrix has a unique factorisation with orthogonal or unitary and upper triangular with positive real diagonal).
The standard real inner product is the dot product (The standard formulas on and on are inner products).
Gram–Schmidt preserves successive spans while normalising orthogonal residuals (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).
Verification
The columns are and . By [L2]–[L3], , while normalises to .
The coefficients are , , and , with . Thus is upper triangular with positive diagonal.
Direct multiplication gives , and . This verifies the displayed QR factorisation.
Bessel's inequality is strict for a vector outside the span of a proper orthonormal set
Example
In standard , take the orthonormal list and . Then Bessel's inequality is strict:
The gap is the squared norm of the orthogonal remainder .
Facts & Assumptions
Given: The first two standard basis vectors and .
Bessel's inequality becomes equality exactly when the vector lies in the span of the orthonormal list (Bessel's inequality for a finite orthonormal list and Parseval's identity for an orthonormal basis).
Standard coordinate inner products are dot products (The standard formulas on and on are inner products).
Verification
By [L2], the two coefficients are and , while . Hence the Bessel sum is and the gap is .
The projection onto is , leaving of squared norm . Since is outside the span, strictness also follows from [L1].
Projection onto a plane in and the nearest-point calculation
Example
Let and . Then
and for every ,
Thus is the unique nearest point of to .
Facts & Assumptions
Given: The displayed plane and vector .
An orthonormal basis of gives (Orthogonal projection is linear, and an orthonormal basis of gives ).
Orthogonal projection is the unique nearest point in the subspace (The orthogonal projection is the unique nearest point in the subspace).
The standard real coordinate inner product is the dot product (The standard formulas on and on are inner products).
Verification
The vectors and form an orthonormal basis of by [L3]. Their coefficients against are and .
Substitution in [L1] gives . The residual is , which is orthogonal to and has squared norm .
For , the residual and are orthogonal, so Pythagoras gives the displayed equality. It is minimised uniquely at , in agreement with [L2].
The Riesz representative of an explicit functional on
Example
For the linear functional
on standard , the Riesz representative under the linear-first convention is
so .
Facts & Assumptions
Given: The displayed linear functional .
Every functional on a finite-dimensional inner product space has a unique vector such that (Finite-dimensional Riesz representation: every functional is uniquely ).
The standard complex inner product is (The standard formulas on and on are inner products).
Verification
By [L2], . Uniqueness in [L1] identifies the displayed vector as the representative.
If the functional is multiplied by , its representative is , since . This explicitly exhibits the conjugate-linear dependence asserted by [L1].
The adjoint of an explicit map is its conjugate-transpose matrix
Example
For the map with standard matrix
the adjoint has matrix
Facts & Assumptions
Given: The displayed complex matrix in standard orthonormal bases.
In orthonormal bases, the matrix of the adjoint is the conjugate transpose (In orthonormal bases, the matrix of the adjoint is the conjugate transpose of the matrix).
The standard complex coordinate pairing is (The standard formulas on and on are inner products).
Verification
Transpose and conjugate each entry. This gives the displayed matrix , exactly as [L1] prescribes.
For generic and , expansion with [L2] gives . Thus the displayed matrix also verifies the adjoint identity directly.
The Gram determinant of two vectors is and detects dependence
Example
For two vectors , the Gram determinant is
In standard , and give determinant . Replacing by gives determinant .
Facts & Assumptions
Given: Two vectors in an inner product space.
A Gram determinant is positive exactly for an independent list and zero exactly for a dependent list (A Gram determinant is nonnegative and is positive exactly when the vector list is linearly independent).
Cauchy–Schwarz says , with equality exactly for dependent (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Verification
Expanding the determinant of and using conjugate symmetry gives the displayed formula. Its nonnegativity and equality case agree with [L1] and [L2].
For , the Gram matrix is , whose determinant is ; this is positive, and are independent, as [L1] requires. For , the two rows are dependent and the determinant is .
A nondegenerate indefinite symmetric form can have
Statement refuted
Every nondegenerate symmetric bilinear form on a finite-dimensional real vector space satisfies for every subspace .
Facts & Assumptions
Given: On , the symmetric form , the vector , and .
A bilinear form is nondegenerate when its radical is zero; for a matrix form this is equivalent to its representing matrix having full rank (The matrix, left and right radicals, rank, and nondegeneracy of a bilinear form on a finite-dimensional space).
The orthogonal-decomposition theorem requires a positive-definite inner product and then gives (For a subspace of a finite-dimensional inner product space, ).
Counterexample
The matrix of is , whose determinant is , so [L1] makes nondegenerate. It is indefinite because and .
Yet . Therefore for every , so the nonzero vector lies in both and . Thus their sum is not direct.
This does not contradict [L2]: positivity, not merely nondegeneracy, is the hypothesis that forces the orthogonal direct sum.
FALSE: Every complex inner product satisfies
Statement
False claim. Every complex inner product satisfies .
Facts & Assumptions
Given: Standard and the vectors , .
Complex inner products are conjugate symmetric: (Real and complex inner product spaces, with the inner product linear in the first argument).
The standard complex inner product is in one dimension (The standard formulas on and on are inner products).
Refutation
By [L2], , whereas . These are unequal.
They are complex conjugates, exactly as [L1] requires. Thus conjugate symmetry, not symmetry, is the correct law.
FALSE: Every orthogonal set is an orthonormal basis
Statement
False claim. Every orthogonal set in an inner product space is an orthonormal basis.
Facts & Assumptions
Given: The singleton list containing in standard .
An orthogonal list only requires distinct members to be pairwise orthogonal; an orthonormal basis additionally requires unit norms and spanning (Orthogonal vectors and subspaces, orthogonal and orthonormal sets, and orthonormal bases).
In standard , the squared norm is the sum of coordinate squares (The standard formulas on and on are inner products).
Refutation
A singleton is orthogonal because it has no distinct pair to check. But [L2] gives , and its span is only a line in .
Thus it is neither normalised nor a basis, exposing both missing requirements in [L1]. The empty orthogonal list supplies the same spanning warning in every nonzero space.
FALSE: Every idempotent endomorphism of an inner product space is an orthogonal projection
Statement
False claim. Every idempotent endomorphism of an inner product space is an orthogonal projection.
Facts & Assumptions
Given: In standard , the matrix .
An endomorphism is an orthogonal projection exactly when it is both idempotent and self-adjoint (An endomorphism is an orthogonal projection exactly when it is idempotent and self-adjoint).
In an orthonormal real basis, the adjoint matrix is the transpose (In orthonormal bases, the matrix of the adjoint is the conjugate transpose of the matrix).
Refutation
Direct multiplication gives , so is idempotent.
But , so [L2] shows that is not self-adjoint. Hence [L1] shows it is not an orthogonal projection.
Concretely, and , whose generators have dot product , so this is an oblique projection.
FALSE: On a complex inner product space, for every scalar
Statement
False claim. On a complex inner product space, for every scalar and linear map .
Facts & Assumptions
Given: The identity map on standard and the scalar .
Adjoint algebra gives , not (Adjoints satisfy , , , and ).
The standard complex inner product is linear in its first argument and conjugate-linear in its second (The standard formulas on and on are inner products).
Refutation
By [L1], , which is not .
Directly, [L2] gives , confirming that the adjoint scalar must be .
Sources
Standard references
Recommended treatments; not extraction sources.
- Sergei Treil, Linear Algebra Done Wrong, Example 5.1.3
- Sergei Treil, Linear Algebra Done Wrong, §5.3.2
- Sheldon Axler, Linear Algebra Done Right, 4th ed., definition 6.2
- Sheldon Axler, Linear Algebra Done Right, 4th ed., definitions 6.22 and 6.27
- Sheldon Axler, Linear Algebra Done Right, 4th ed., §§6C and 7A
- Sheldon Axler, Linear Algebra Done Right, 4th ed., result 7.5