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✓ 8 results · all verified · 3 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 5 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Bounded Linear Operators and Quotient Spaces — Examples

1 · Prerequisites

2 · Summary

The companion page keeps the bounded-operator and quotient package concrete: product projections, shifts, and evaluation functionals; a standard unbounded differentiation map; the quotient-by-kernel model of a range; the oscillation norm on functions modulo constants; and two guardrails against over-reading the theory, one analytic and one algebraic.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passaudited 2026-09-01Open item page →

Coordinate projections and inclusions on a finite product Banach space

Example

Let X and Y be Banach spaces over the same scalar field and equip X×Y with the maximum product norm

∥(x,y)∥max⁡:=max⁡{∥x∥,∥y∥}.

Then the coordinate projections

πX(x,y)=x,πY(x,y)=y,

and the coordinate inclusions

ιX(x)=(x,0),ιY(y)=(0,y)

are bounded linear operators. Moreover,

∥πX∥=∥ιX∥={1,X≠{0},0,X={0},∥πY∥=∥ιY∥={1,Y≠{0},0,Y={0}.

Facts & Assumptions

Given: Banach spaces X and Y over the same scalar field, their product X×Y with the maximum norm, and vectors x∈X, y∈Y.

[L1]

The maximum product norm is one of the standard product norms (The standard product norms on a finite product of normed spaces).

[L2]

A finite product of Banach spaces is Banach, and bounded linear operators are the members of B(⋅,⋅) (Finite products of Banach spaces are Banach, A bounded linear operator between normed spaces, The spaces (\mathcal B(X,Y)) and (\mathcal B(X)) of bounded linear operators).

[L3]

The operator norm is the unit-ball supremum and therefore records the least global bound of a bounded linear operator (The operator norm as the least bound and as the unit-sphere or unit-ball supremum).

Verification

technique · direct
1.1L1L2L3

By [L1], ∥(x,y)∥max⁡≥∥x∥ and ∥(x,y)∥max⁡≥∥y∥, so ∥πX(x,y)∥≤∥(x,y)∥max⁡ and ∥πY(x,y)∥≤∥(x,y)∥max⁡. Thus both projections are bounded with operator norm at most 1 by [L2] and [L3].

2.1step 1.1L1L3choose

For x∈X and y∈Y, [L1] gives ∥ιX(x)∥max⁡=∥(x,0)∥max⁡=∥x∥ and ∥ιY(y)∥max⁡=∥(0,y)∥max⁡=∥y∥. So ∥ιX∥≤1 and ∥ιY∥≤1, with equality whenever the relevant domain is nonzero. If X≠{0}, choose u∈X with ∥u∥=1. Then ∥ιX(u)∥max⁡=1, and also ∥πX(u,0)∥=1=∥(u,0)∥max⁡, so ∥ιX∥=∥πX∥=1. If X={0}, then both ιX and πX are the zero operator, so both norms are 0. The same argument with Y gives the corresponding statements for ιY and πY.

3.1step 1.1step 2.1L2∎

Therefore πX∈B(X×Y,X), πY∈B(X×Y,Y), ιX∈B(X,X×Y), and ιY∈B(Y,X×Y), with the norms stated above.

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-01Open item page →

Forward and backward shifts on classical sequence spaces and their exact operator norms

Example

On either of the normed spaces

c0:={x=(xn)n≥0:xn→0}orℓ∞:={x=(xn)n≥0:sup⁡n∣xn∣<∞},

equipped with the supremum norm ∥x∥∞:=sup⁡n∣xn∣, define the forward and backward shifts by

F(x0,x1,x2,… ):=(0,x0,x1,… ), B(x0,x1,x2,… ):=(x1,x2,x3,… ).

Then F and B are bounded linear operators and

∥F∥=∥B∥=1.

Facts & Assumptions

Given: One of the normed spaces c0 or ℓ∞ with the supremum norm, and a sequence x=(xn)n≥0 in that space.

[L2]

A bounded linear operator is a linear map with a uniform norm bound (A bounded linear operator between normed spaces).

[L3]

The operator norm is the least global bound, equivalently the unit-ball supremum (The operator norm as the least bound and as the unit-sphere or unit-ball supremum).

Verification

technique · direct
1.1L2L3

On either space, F and B are linear by coordinatewise inspection. Also ∥Fx∥∞≤∥x∥∞ and ∥Bx∥∞≤∥x∥∞, so both operators are bounded with operator norm at most 1 by [L2] and [L3].

2.1step 1.1

The vectors e0=(1,0,0,… ) and e1=(0,1,0,… ) lie in both spaces. They satisfy ∥Fe0∥∞=∥e0∥∞=1 and ∥Be1∥∞=∥e0∥∞=1, so ∥F∥≥1 and ∥B∥≥1.

3.1step 1.1step 2.1∎

Combining steps 1.1 and 2.1 gives ∥F∥=∥B∥=1 on both c0 and ℓ∞.

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passaudited 2026-09-01Open item page →

The evaluation functional on (C(K)) has norm one

Example

Let K be a nonempty compact metric space, let K∈{R,C} be the scalar field, let C(K,K) carry the supremum norm, and let x0∈K. Define

δx0:C(K,K)→K,δx0(f):=f(x0).

Then δx0 is a bounded linear functional of norm 1.

Facts & Assumptions

Given: A nonempty compact metric space K, a scalar field K∈{R,C}, a point x0∈K, and the space C(K,K) of continuous K-valued functions on K with the supremum norm.

[L2]

A bounded linear operator is a linear map with a uniform norm bound (A bounded linear operator between normed spaces).

[L3]

The operator norm is the least global bound, equivalently the unit-ball supremum (The operator norm as the least bound and as the unit-sphere or unit-ball supremum).

Verification

technique · direct
1.1L2L3

The map δx0 is linear, and for every f∈C(K,K), ∣δx0(f)∣=∣f(x0)∣≤∥f∥∞. Hence δx0 is bounded with operator norm at most 1 by [L2] and [L3].

2.1step 1.1∎

Let 1 be the constant function 1 on K. Then ∥1∥∞=1 and ∣δx0(1)∣=1, so ∥δx0∥≥1. Therefore ∥δx0∥=1.

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passaudited 2026-09-01Open item page →

Differentiation on polynomials is unbounded for the supremum norm

Example

Let P[0,1] be the real polynomial functions on [0,1] with the supremum norm, and let

D:P[0,1]→P[0,1],Dp:=p′.

Then D is linear but not bounded for the supremum norm.

Facts & Assumptions

Given: The differentiation operator D on P[0,1] and the polynomials pn(x):=xn.

[L1]

A bounded linear operator requires one constant C such that ∥Dp∥∞≤C∥p∥∞ for every polynomial p (A bounded linear operator between normed spaces).

Verification

technique · direct
1.1givenalgebra

Differentiation is linear on polynomials. For each n≥1, ∥pn∥∞=1 on [0,1], while Dpn(x)=nxn−1 and therefore ∥Dpn∥∞=n.

2.1step 1.1L1assume-contradischarge-contradiction∎

If D were bounded, [L1] would give a constant C with n=∥Dpn∥∞≤C∥pn∥∞=C for every n≥1, impossible. Hence D is unbounded.

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passaudited 2026-09-01Open item page →

The quotient by the kernel is isometric to the range with its induced quotient norm

Example

Let T:X→Y be a bounded linear operator between normed spaces, and write M:=ker⁡T. The factor map

T‾:X/M→im⁡T,T‾(x+M):=Tx,

is a linear isomorphism. If im⁡T is equipped with the induced quotient norm

∥Tx∥quot:=∥x+M∥X/M,

then T‾ is an isometry.

Facts & Assumptions

Given: A bounded linear operator T:X→Y and its kernel M:=ker⁡T.

[L1]

A bounded linear operator is in particular linear (A bounded linear operator between normed spaces).

[L2]

The kernel and image are the sets ker⁡T={x:Tx=0} and im⁡T={Tx:x∈X} (Kernel and image of a linear map).

[L3]

The kernel is a linear subspace, and a linear map is injective exactly when its kernel is trivial (The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial).

[L4]

The quotient seminorm is the infimum over representatives (The quotient seminorm (|x+M|{X/M}=\inf{m\in M}|x+m|=\operatorname{dist}(x,M))).

[L5]

A linear map that vanishes on a subspace factors uniquely through the algebraic quotient (Universal property of the quotient vector space).

Verification

technique · direct
1.1L1L2L3L5

Since T is linear by [L1], the set M=ker⁡T is a linear subspace by [L3], and certainly M⊆ker⁡T. Therefore [L5] gives a unique linear map T‾:X/M→Y with T‾(x+M)=Tx. Its range is exactly im⁡T, so we may read it as a map into im⁡T.

2.1step 1.1L2L3

If T‾(x+M)=0, then Tx=0, so x∈ker⁡T=M by [L2]. Hence x+M=M, the zero coset. Therefore T‾ is injective by [L3].

2.2step 1.1L2L4

If Tx=Ty, then x−y∈ker⁡T=M by [L2], so x+M=y+M. Thus the formula ∥Tx∥quot:=∥x+M∥X/M is well defined on im⁡T, using the quotient seminorm of [L4]. By construction, ∥T‾(x+M)∥quot=∥x+M∥X/M for every coset, so T‾ is an isometry onto im⁡T.

3.1step 1.1step 2.1step 2.2∎

Steps 1.1, 2.1, and 2.2 show that X/ker⁡T is linearly isomorphic to the range of T, and isometric once the range is given the induced quotient norm.

ExampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-01Open item page →

For real continuous functions modulo constants, the quotient norm is half the oscillation

Example

Let K be a nonempty compact metric space, let C(K,R) carry the supremum norm, and let M be the subspace of constant functions. For f∈C(K,R) write

osc⁡(f):=max⁡Kf−min⁡Kf.

Then in the quotient C(K,R)/M,

∥f+M∥=12osc⁡(f).

Facts & Assumptions

Given: A nonempty compact metric space K, a real-valued continuous function f on K, and the constant-function subspace M.

[L1]

The quotient seminorm is ∥f+M∥=inf⁡c∈R∥f−c∥∞ (The quotient seminorm (|x+M|{X/M}=\inf{m\in M}|x+m|=\operatorname{dist}(x,M))).

[L2]

A continuous real-valued function on a nonempty compact metric space attains its maximum and minimum (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).

[L3]

The quotient seminorm is a norm when the subspace is closed (The quotient seminorm is a norm exactly when the subspace is closed).

Verification

technique · direct
1.1L2algebra

By [L2], let m:=min⁡Kf and M0:=max⁡Kf, and put c0:=(M0+m)/2. Then for every x∈K, m≤f(x)≤M0, so ∣f(x)−c0∣≤(M0−m)/2. Hence ∥f−c0∥∞≤(M0−m)/2.

2.1step 1.1L2algebra

For any real constant c, both ∣M0−c∣ and ∣m−c∣ are bounded above by ∥f−c∥∞. Since the distance between M0 and m is M0−m, at least one of those two numbers is at least (M0−m)/2. Therefore ∥f−c∥∞≥(M0−m)/2 for every c.

3.1step 1.1step 2.1L1L3∎

Steps 1.1 and 2.1 give inf⁡c∈R∥f−c∥∞=(M0−m)/2, so [L1] yields ∥f+M∥=osc⁡(f)/2. The constant subspace is closed because a uniform limit of constant functions is constant, so [L3] confirms that this is an honest norm on the quotient.

CounterexampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passaudited 2026-09-01Open item page →

A closed subspace of a Banach space need not contain a nearest point to every ambient vector

Statement refuted

Refuted claim: every closed linear subspace of a Banach space contains a nearest point to every ambient vector.

In the real Banach space

c0:={x=(xn)n≥0:xn∈R, xn→0}

with the supremum norm, define

φ(x):=∑n=0∞2−n−1xn,

let

H:=ker⁡φ,

and set x:=2e0=(2,0,0,… ). Then H is a closed linear subspace of c0, the distance from x to H is 1, and no point of H realizes that distance.

Facts & Assumptions

Given: The real normed space c0, the functional φ, its kernel H, and the vector x:=2e0.

Counterexample

technique · direct
1.1constructalgebra

Let (x(m)) be a Cauchy sequence in c0 for the supremum norm, with coordinates x(m)=(xn(m))n≥0. For each n, the scalar sequence (xn(m))m is Cauchy because ∣xn(m)−xn(ℓ)∣≤∥x(m)−x(ℓ)∥∞. Since the scalars are real, let zn:=lim⁡mxn(m). Choosing M0 with ∥x(m)−x(ℓ)∥∞<1 for m,ℓ≥M0 and letting ℓ→∞ gives ∣zn∣≤∥x(M0)∥∞+1 for every n, so z:=(zn) is bounded. Given ε>0, choose M such that ∥x(m)−x(ℓ)∥∞<ε/2 whenever m,ℓ≥M. Letting ℓ→∞ coordinatewise gives ∣xn(m)−zn∣≤ε/2 for every n and every m≥M, hence ∥x(m)−z∥∞≤ε/2. Fix such an m and choose N with ∣xn(m)∣<ε/2 for all n≥N, since x(m)∈c0. Then ∣zn∣<ε for n≥N, so z∈c0. The displayed uniform estimate also gives ∥x(m)−z∥∞→0. Therefore c0 is Banach.

2.1step 1.1algebra

The series defining φ converges absolutely and ∣φ(z)∣≤∑n=0∞2−n−1∣zn∣≤∥z∥∞ for every z∈c0, so φ is a bounded linear functional with ∥φ∥≤1. If z(m)∈H and z(m)→z in c0, then ∣φ(z)∣=∣φ(z−z(m))∣≤∥z−z(m)∥∞→0, so z∈H. Thus H is a closed linear subspace. Also φ(x)=1.

3.1step 2.1algebra

For every y∈H one has φ(x−y)=1, hence 1=∣φ(x−y)∣≤∥x−y∥∞ by step 2.1. Therefore dist⁡(x,H)≥1.

3.2step 2.1construct

For N≥1, define z(N)∈c0 by zn(N):=(1−2−N)−1 for 0≤n<N and zn(N):=0 for n≥N. Then φ(z(N))=(1−2−N)−1∑n=0N−12−n−1=1, so y(N):=x−z(N) lies in H, and ∥x−y(N)∥∞=∥z(N)∥∞=(1−2−N)−1→1. Hence dist⁡(x,H)≤1.

4.1step 2.1step 3.1step 3.2assume-contraalgebra

Steps 3.1 and 3.2 give dist⁡(x,H)=1. Suppose some y∈H satisfied ∥x−y∥∞=1, and put z:=x−y. Then ∥z∥∞=1 and φ(z)=1 by step 2.1. Also 1=φ(z)=∑n=0∞2−n−1zn≤∑n=0∞2−n−1∣zn∣≤∑n=0∞2−n−1∥z∥∞=1.

5.1step 4.1discharge-contradiction

So equality holds throughout in step 4.1, forcing ∣zn∣=1 and zn≥0 for every n. That contradicts z∈c0, since a sequence converging to 0 cannot have all coordinates equal to 1 in modulus.

6.1step 1.1step 2.1step 3.1step 3.2step 5.1∎

Thus H is a closed subspace of the Banach space c0, the distance from x to H is 1, and no y∈H attains it. This refutes the claim.

CounterexampleConstruction: Literature-sourcedVerification: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-01Open item page →

An algebraic complement need not be a topological complement

Statement refuted

Refuted claim: every algebraic direct-sum decomposition of a normed space is automatically a topological direct sum.

Assume the Axiom of Choice and fix a Hamel basis H of c0 containing all standard unit vectors en. Let M:=span⁡{e0}, let W be the kernel of the linear map P:c0→M defined on basis vectors by

P(e0)=e0,P(en)=ne0 for n≥1,P(h)=0 for h∈H∖{en:n≥0},

and extend linearly. Then c0=M⊕W algebraically, but W is not a topological complement of M.

Facts & Assumptions

Given: The normed space c0, the Hamel basis H, the one-dimensional subspace M=span⁡{e0}, and the algebraic projection P above.

[L2]

A topological complement is a direct-sum partner with bounded coordinate projections (A complemented closed subspace of a normed space).

Counterexample

technique · direct
1.1givenalgebra

By construction, P is linear, P2=P, and ran⁡(P)=M. So W:=ker⁡P satisfies c0=M⊕W algebraically: every vector decomposes as Px+(x−Px), and the intersection is trivial because P acts as the identity on M and vanishes on W.

2.1step 1.1

The projection P is not bounded for the supremum norm. Indeed, un:=en/n satisfies ∥un∥∞=1/n→0, while P(un)=e0 for every n≥1, so ∥P(un)∥∞=1. No bounded linear map can behave this way at 0.

3.1step 1.1step 2.1L2assume-contradischarge-contradiction

Suppose, toward a contradiction, that W were a topological complement of M. Then [L2] gives a bounded projection onto M along W, and that projection is unique because the decomposition x=m+w with m∈M, w∈W determines the projection value m pointwise. But P already has exactly that range and kernel by step 1.1, so the bounded projection would have to equal P, contradicting step 2.1.

4.1step 1.1step 3.1∎

Therefore c0=M⊕W is an algebraic decomposition that is not a topological direct sum. This refutes the claim.

Remarks

  • The example is intentionally non-load-bearing: it uses a Hamel basis and therefore the Axiom of Choice.
  • The point is not that complements are rare, but that boundedness of the coordinate projections is extra structure and must be stated.

Sources