Alphabeta Math
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16 results · all verified · 10 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 6 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Inner Product Spaces and Orthogonality: Examples and Counterexamples

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

Positive coordinate weights define an inner product and change lengths and projections

Example

For positive real weights a0,,an1,

x,ya=k<nakxkyk

is an inner product on Fn, where F=R or C. With weights (1,4) on R2, the vector (1,1) has length 5, and the projection of (1,0) onto span(1,1) is (1/5,1/5) rather than the standard projection (1/2,1/2).

Facts & Assumptions

Given: Positive weights ak and the displayed pairing.

[L1]

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).

[L3]

For an orthonormal basis (ei)i<r of a subspace W of a finite-dimensional inner product space, PWv=i<rv,eiei (Orthogonal projection is linear, and an orthonormal basis (ei) of W gives PWv=iv,eiei).

Verification

technique · direct
1.1

Finite-sum algebra gives linearity, conjugate symmetry, and x,xa=kakxk20. Because every ak>0, this sum vanishes exactly when every coordinate of x vanishes, so [L1] holds. For n=0, [L4] leaves only the zero vector.

L1L4algebra
2.1

For weights (1,4), [L2] gives (1,1)a2=1+4=5. Put u=(1,1), v=(1,0) and W=span(u). Then e=ua1u is an orthonormal basis of W, so [L3] gives PWv=v,eae=v,uau,ua1u. Here v,ua=1 and u,ua=5, so PWv=(1/5)u. With both weights 1 the same computation uses v,u=1 and u,u=2 and gives (1/2)u.

L2L3algebra
ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passaudited 2026-08-16Open item page →

The Frobenius inner product on a real or complex matrix space

Example

On Mm×n(F), where F=R or C, the formula

A,BF=i<mj<nAijBij

defines the Frobenius inner product, and

AF2=i<mj<nAij2.

Facts & Assumptions

Given: Matrices A,B of one fixed m×n shape.

[L1]

Matrices of a fixed shape form a vector space under entrywise operations (The vector space Mm×n(F):=Fm×n of m by n matrices over a field, with entrywise operations).

[L2]

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).

[L3]

Complex conjugation distributes over finite sums and satisfies zz=z2, vanishing exactly at z=0 (Conjugation is an involutive real-field automorphism, zz=z2, and modulus is definite, multiplicative, and subadditive).

Verification

technique · direct
1.1

Entrywise operations and finite-sum algebra give linearity in A. Applying conjugation termwise and using [L3] gives conjugate symmetry.

L1L3
1.2

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 A=0. Hence [L2] is satisfied.

L1L2L3
2.1

If m=0 or n=0, the matrix space contains only its zero matrix and [L4] makes the formula zero, so definiteness remains valid.

L1L4
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Evaluation at four distinct real points gives an inner product on polynomials of degree at most three

Example

On the real vector space P3(R) of polynomials of degree at most 3, evaluation at four distinct real points t0,t1,t2,t3 defines an inner product by

p,q=k<4p(tk)q(tk).

For example, the points may be 0,1,2,3.

Facts & Assumptions

Given: Four distinct real scalars t0,t1,t2,t3 and polynomials p,qP3(R).

[L1]

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).

[L2]

A nonzero polynomial over a domain has at most as many distinct roots as its degree (A nonzero polynomial of degree n over an integral domain has at most n distinct roots).

Verification

technique · direct
1.1

By [L3], the displayed formula is bilinear and symmetric. Also p,p=k<4p(tk)20.

L1L3
2.1

If p,p=0, every real square in the sum is zero, so p(tk)=0 for four distinct tk. If p were nonzero, [L2] would give at most three roots because degp3. Thus p=0, proving definiteness and hence [L1].

step 1.1L1L2
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Gram–Schmidt on an explicit basis of R3

Example

Applying Gram–Schmidt in standard R3 to

v0=(1,1,1),v1=(0,1,2),v2=(1,0,2)

produces

e0=(1,1,1)3,e1=(1,0,1)2,e2=(1,2,1)6.

Facts & Assumptions

Given: The displayed ordered basis in standard R3.

[L2]

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

technique · computation
1.1

By [L1], v0=3, giving e0. The second residual is u1=v1v1,e0e0=(1,0,1), whose norm is 2, giving e1.

L1L2algebra
1.2

The third residual is u2=v2v2,e0e0v2,e1e1=(1/2,1,1/2). Its norm is 6/2, so normalisation gives the displayed e2.

L1L2algebra
2.1

Direct dot products give ei,ej=δij. Moreover e0 is a nonzero multiple of v0, u1=v1v0, and u2 differs from v2 by a combination of the first two residual directions, verifying all three successive-span equalities from [L2].

step 1.1step 1.2L1L2
ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

Gram–Schmidt on an explicit basis of C2 with conjugation visible

Example

For the linear-first standard inner product on C2, Gram–Schmidt applied to v0=(1,i) and v1=(1,1) produces

e0=(1,i)2,e1=(1+i2,1i2).

Facts & Assumptions

Given: The vectors v0=(1,i) and v1=(1,1) in C2.

[L2]

Gram–Schmidt uses the coefficient vk,ej under the linear-first convention (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).

Verification

technique · computation
1.1

From [L1], v0=2, so e0=v0/2. The projection coefficient is v1,e0=(1i)/2.

L1L2L3
2.1

Subtraction gives u1=v1v1,e0e0=((1+i)/2,(1i)/2). By [L3], u12=1, so e1=u1.

step 1.1L1L3algebra
3.1

Direct use of [L1] gives e1,e0=0 and, by conjugate symmetry, e0,e1=0. Both vectors have norm one, so the displayed list is orthonormal.

step 1.1step 2.1L1L3
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A worked QR factorisation of an invertible real matrix

Example

The invertible matrix

A=(1110)

has the QR factorisation

A=QR,Q=12(1111),R=(21/201/2).

Facts & Assumptions

Given: The displayed real matrix A.

[L1]

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 A=QR with Q orthogonal or unitary and R upper triangular with positive real diagonal).

[L3]

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

technique · computation
1.1

The columns are a0=(1,1) and a1=(1,0). By [L2]–[L3], q0=(1,1)/2, while a1a1,q0q0=(1/2,1/2) normalises to q1=(1,1)/2.

L2L3algebra
2.1

The coefficients are R00=2, R01=1/2, and R11=1/2, with R10=0. Thus R is upper triangular with positive diagonal.

step 1.1L1
3.1

Direct multiplication gives QR=A, and QTQ=I. This verifies the displayed QR factorisation.

step 1.1step 2.1algebra
ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

Bessel's inequality is strict for a vector outside the span of a proper orthonormal set

Example

In standard R3, take the orthonormal list (e0,e1) and v=(1,2,3). Then Bessel's inequality is strict:

v,e02+v,e12=5<14=v2.

The gap 9 is the squared norm of the orthogonal remainder 3e2.

Facts & Assumptions

Given: The first two standard basis vectors and v=(1,2,3).

[L1]

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).

Verification

technique · computation
1.1

By [L2], the two coefficients are 1 and 2, while v2=12+22+32=14. Hence the Bessel sum is 5 and the gap is 9.

L2algebra
2.1

The projection onto span(e0,e1) is (1,2,0), leaving 3e2 of squared norm 9. Since v is outside the span, strictness also follows from [L1].

step 1.1L1L2
ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-08-16Open item page →

Projection onto a plane in R3 and the nearest-point calculation

Example

Let W={(x,y,z)R3:x+y+z=0} and v=(1,2,3). Then

PWv=(1,0,1),

and for every qW,

vq2=12+(1,0,1)q2.

Thus (1,0,1) is the unique nearest point of W to v.

Facts & Assumptions

Given: The displayed plane W and vector v.

[L1]

An orthonormal basis (ui) of W gives PWv=iv,uiui (Orthogonal projection is linear, and an orthonormal basis (ei) of W gives PWv=iv,eiei).

[L2]

Orthogonal projection is the unique nearest point in the subspace (The orthogonal projection is the unique nearest point in the subspace).

Verification

technique · computation
1.1

The vectors u0=(1,1,0)/2 and u1=(1,1,2)/6 form an orthonormal basis of W by [L3]. Their coefficients against v are 1/2 and 3/6.

L3algebra
2.1

Substitution in [L1] gives PWv=(1,0,1). The residual is (2,2,2), which is orthogonal to W and has squared norm 12.

step 1.1L1L3
3.1

For qW, the residual and PWvq are orthogonal, so Pythagoras gives the displayed equality. It is minimised uniquely at q=PWv, in agreement with [L2].

step 2.1L2
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The Riesz representative of an explicit functional on C3

Example

For the linear functional

f(z0,z1,z2)=(1+i)z02iz1+3z2

on standard C3, the Riesz representative under the linear-first convention is

w=(1i,2i,3),

so f(z)=z,w.

Facts & Assumptions

Given: The displayed linear functional f.

[L1]

Every functional on a finite-dimensional inner product space has a unique vector w such that f(z)=z,w (Finite-dimensional Riesz representation: every functional is uniquely vv,w).

Verification

technique · computation
1.1

By [L2], z,(1i,2i,3)=(1+i)z02iz1+3z2=f(z). Uniqueness in [L1] identifies the displayed vector as the representative.

L1L2algebra
2.1

If the functional is multiplied by i, its representative is iw, since z,iw=iz,w. This explicitly exhibits the conjugate-linear dependence asserted by [L1].

step 1.1L1L2
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The adjoint of an explicit map C2C3 is its conjugate-transpose matrix

Example

For the map T:C2C3 with standard matrix

A=(1i2i0i3),

the adjoint T:C3C2 has matrix

A=(12+iii03).

Facts & Assumptions

Given: The displayed complex matrix A in standard orthonormal bases.

[L1]

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).

[L2]

The standard complex coordinate pairing is x,y=kxkyk (The standard formulas x,y=k<nxkyk on Rn and k<nxkyk on Cn are inner products).

Verification

technique · computation
1.1

Transpose A and conjugate each entry. This gives the displayed 2×3 matrix A, exactly as [L1] prescribes.

L1algebra
2.1

For generic xC2 and yC3, expansion with [L2] gives Ax,y=xTATy=x,Ay. Thus the displayed matrix also verifies the adjoint identity directly.

step 1.1L2algebra
ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-08-16Open item page →

The Gram determinant of two vectors is u2v2u,v2 and detects dependence

Example

For two vectors u,v, the Gram determinant is

detG(u,v)=u2v2u,v2.

In standard R2, u=(1,0) and v=(1,2) give determinant 4. Replacing v by 2u gives determinant 0.

Facts & Assumptions

Given: Two vectors u,v in an inner product space.

[L1]

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).

[L2]

Cauchy–Schwarz says u,v2u2v2, with equality exactly for dependent u,v (Cauchy–Schwarz: u,vuv, with equality exactly for linearly dependent vectors).

Verification

technique · computation
1.1

Expanding the determinant of (u,uu,vv,uv,v) and using conjugate symmetry gives the displayed formula. Its nonnegativity and equality case agree with [L1] and [L2].

L1L2algebra
2.1

For (1,0),(1,2), the Gram matrix is (1115), whose determinant is 4; this is positive, and (1,0),(1,2) are independent, as [L1] requires. For (u,2u), the two rows are dependent and the determinant is 0.

step 1.1L1algebra
CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

A nondegenerate indefinite symmetric form can have WW0

Statement refuted

Every nondegenerate symmetric bilinear form B on a finite-dimensional real vector space satisfies V=WWB for every subspace W.

Facts & Assumptions

Given: On R2, the symmetric form B(x,y)=x0y0x1y1, the vector v=(1,1), and W=Rv.

[L1]

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).

[L2]

The orthogonal-decomposition theorem requires a positive-definite inner product and then gives V=WW (For a subspace W of a finite-dimensional inner product space, V=WW).

Counterexample

technique · counterexample
1.1

The matrix of B is diag(1,1), whose determinant is 1, so [L1] makes B nondegenerate. It is indefinite because B((1,0),(1,0))=1 and B((0,1),(0,1))=1.

L1algebra
1.2

Yet B(v,v)=11=0. Therefore B(v,av)=0 for every avW, so the nonzero vector v lies in both W and WB. Thus their sum is not direct.

algebra
2.1

This does not contradict [L2]: positivity, not merely nondegeneracy, is the hypothesis that forces the orthogonal direct sum.

step 1.1step 1.2L2
False statementConstruction: AI-adaptedVerification: AI-adaptedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

FALSE: Every complex inner product satisfies u,v=v,u

Statement

False claim. Every complex inner product satisfies u,v=v,u.

Facts & Assumptions

Given: Standard C and the vectors u=i, v=1.

[L1]

Complex inner products are conjugate symmetric: u,v=v,u (Real and complex inner product spaces, with the inner product linear in the first argument).

Refutation

technique · counterexample
1.1

By [L2], i,1=i, whereas 1,i=i. These are unequal.

L2algebra
2.1

They are complex conjugates, exactly as [L1] requires. Thus conjugate symmetry, not symmetry, is the correct law.

step 1.1L1
False statementConstruction: AI-adaptedVerification: AI-adaptedprecheck passaudited 2026-08-16Open item page →

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 (2,0) in standard R2.

[L1]

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).

Refutation

technique · counterexample
1.1

A singleton is orthogonal because it has no distinct pair to check. But [L2] gives (2,0)=2, and its span is only a line in R2.

L2
2.1

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.

step 1.1L1
False statementConstruction: AI-adaptedVerification: AI-adaptedprecheck passaudited 2026-08-16Open item page →

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 R2, the matrix P=(1100).

[L1]

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).

[L2]

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

technique · counterexample
1.1

Direct multiplication gives P2=P, so P is idempotent.

algebra
1.2

But PT=(1010)P, so [L2] shows that P is not self-adjoint. Hence [L1] shows it is not an orthogonal projection.

L1L2
2.1

Concretely, imP=span(1,0) and kerP=span(1,1), whose generators have dot product 1, so this is an oblique projection.

algebra
False statementConstruction: AI-adaptedVerification: AI-adaptedprecheck passjudge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-16Open item page →

FALSE: On a complex inner product space, (λT)=λT for every scalar

Statement

False claim. On a complex inner product space, (λT)=λT for every scalar λ and linear map T.

Facts & Assumptions

Given: The identity map I on standard C and the scalar λ=i.

[L2]

The standard complex inner product is linear in its first argument and conjugate-linear in its second (The standard formulas x,y=k<nxkyk on Rn and k<nxkyk on Cn are inner products).

Refutation

technique · counterexample
1.1

By [L1], (iI)=iI=iI, which is not iI.

L1algebra
2.1

Directly, [L2] gives iz,w=iz,w=z,iw, confirming that the adjoint scalar must be i.

step 1.1L2

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