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Bounded Linear Operators and Quotient Spaces — Examples
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- 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
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- 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
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Suprema and Infima
- The ZFC Axioms and the Basic Set Constructions
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
Coordinate projections and inclusions on a finite product Banach space
Example
Let and be Banach spaces over the same scalar field and equip with the maximum product norm
Then the coordinate projections
and the coordinate inclusions
are bounded linear operators. Moreover,
Facts & Assumptions
Given: Banach spaces and over the same scalar field, their product with the maximum norm, and vectors , .
The maximum product norm is one of the standard product norms (The standard product norms on a finite product of normed spaces).
A finite product of Banach spaces is Banach, and bounded linear operators are the members of (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).
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
By [L1], and , so and . Thus both projections are bounded with operator norm at most by [L2] and [L3].
For and , [L1] gives and . So and , with equality whenever the relevant domain is nonzero. If , choose with . Then , and also , so . If , then both and are the zero operator, so both norms are . The same argument with gives the corresponding statements for and .
Therefore , , , and , with the norms stated above.
Forward and backward shifts on classical sequence spaces and their exact operator norms
Example
On either of the normed spaces
equipped with the supremum norm , define the forward and backward shifts by
Then and are bounded linear operators and
Facts & Assumptions
Given: One of the normed spaces or with the supremum norm, and a sequence in that space.
A bounded linear operator is a linear map with a uniform norm bound (A bounded linear operator between normed spaces).
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
On either space, and are linear by coordinatewise inspection. Also and , so both operators are bounded with operator norm at most by [L2] and [L3].
The vectors and lie in both spaces. They satisfy and , so and .
Combining steps 1.1 and 2.1 gives on both and .
The evaluation functional on (C(K)) has norm one
Example
Let be a nonempty compact metric space, let be the scalar field, let carry the supremum norm, and let . Define
Then is a bounded linear functional of norm .
Facts & Assumptions
Given: A nonempty compact metric space , a scalar field , a point , and the space of continuous -valued functions on with the supremum norm.
A bounded linear operator is a linear map with a uniform norm bound (A bounded linear operator between normed spaces).
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
The map is linear, and for every , . Hence is bounded with operator norm at most by [L2] and [L3].
Let be the constant function on . Then and , so . Therefore .
Differentiation on polynomials is unbounded for the supremum norm
Example
Let be the real polynomial functions on with the supremum norm, and let
Then is linear but not bounded for the supremum norm.
Facts & Assumptions
Given: The differentiation operator on and the polynomials .
A bounded linear operator requires one constant such that for every polynomial (A bounded linear operator between normed spaces).
Verification
Differentiation is linear on polynomials. For each , on , while and therefore .
If were bounded, [L1] would give a constant with for every , impossible. Hence is unbounded.
The quotient by the kernel is isometric to the range with its induced quotient norm
Example
Let be a bounded linear operator between normed spaces, and write . The factor map
is a linear isomorphism. If is equipped with the induced quotient norm
then is an isometry.
Facts & Assumptions
Given: A bounded linear operator and its kernel .
A bounded linear operator is in particular linear (A bounded linear operator between normed spaces).
The kernel and image are the sets and (Kernel and image of a linear map).
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).
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))).
A linear map that vanishes on a subspace factors uniquely through the algebraic quotient (Universal property of the quotient vector space).
Verification
Since is linear by [L1], the set is a linear subspace by [L3], and certainly . Therefore [L5] gives a unique linear map with . Its range is exactly , so we may read it as a map into .
If , then , so by [L2]. Hence , the zero coset. Therefore is injective by [L3].
If , then by [L2], so . Thus the formula is well defined on , using the quotient seminorm of [L4]. By construction, for every coset, so is an isometry onto .
Steps 1.1, 2.1, and 2.2 show that is linearly isomorphic to the range of , and isometric once the range is given the induced quotient norm.
For real continuous functions modulo constants, the quotient norm is half the oscillation
Example
Let be a nonempty compact metric space, let carry the supremum norm, and let be the subspace of constant functions. For write
Then in the quotient ,
Facts & Assumptions
Given: A nonempty compact metric space , a real-valued continuous function on , and the constant-function subspace .
The quotient seminorm is (The quotient seminorm (|x+M|{X/M}=\inf{m\in M}|x+m|=\operatorname{dist}(x,M))).
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).
The quotient seminorm is a norm when the subspace is closed (The quotient seminorm is a norm exactly when the subspace is closed).
Verification
By [L2], let and , and put . Then for every , , so . Hence .
For any real constant , both and are bounded above by . Since the distance between and is , at least one of those two numbers is at least . Therefore for every .
Steps 1.1 and 2.1 give , so [L1] yields . 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.
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
with the supremum norm, define
let
and set . Then is a closed linear subspace of , the distance from to is , and no point of realizes that distance.
Facts & Assumptions
Given: The real normed space , the functional , its kernel , and the vector .
Counterexample
Let be a Cauchy sequence in for the supremum norm, with coordinates . For each , the scalar sequence is Cauchy because . Since the scalars are real, let . Choosing with for and letting gives for every , so is bounded. Given , choose such that whenever . Letting coordinatewise gives for every and every , hence . Fix such an and choose with for all , since . Then for , so . The displayed uniform estimate also gives . Therefore is Banach.
The series defining converges absolutely and for every , so is a bounded linear functional with . If and in , then , so . Thus is a closed linear subspace. Also .
For every one has , hence by step 2.1. Therefore .
For , define by for and for . Then , so lies in , and . Hence .
Steps 3.1 and 3.2 give . Suppose some satisfied , and put . Then and by step 2.1. Also
So equality holds throughout in step 4.1, forcing and for every . That contradicts , since a sequence converging to cannot have all coordinates equal to in modulus.
Thus is a closed subspace of the Banach space , the distance from to is , and no attains it. This refutes the claim.
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 of containing all standard unit vectors . Let , let be the kernel of the linear map defined on basis vectors by
and extend linearly. Then algebraically, but is not a topological complement of .
Facts & Assumptions
Given: The normed space , the Hamel basis , the one-dimensional subspace , and the algebraic projection above.
A topological complement is a direct-sum partner with bounded coordinate projections (A complemented closed subspace of a normed space).
Counterexample
By construction, is linear, , and . So satisfies algebraically: every vector decomposes as , and the intersection is trivial because acts as the identity on and vanishes on .
The projection is not bounded for the supremum norm. Indeed, satisfies , while for every , so . No bounded linear map can behave this way at .
Suppose, toward a contradiction, that were a topological complement of . Then [L2] gives a bounded projection onto along , and that projection is unique because the decomposition with , determines the projection value pointwise. But already has exactly that range and kernel by step 1.1, so the bounded projection would have to equal , contradicting step 2.1.
Therefore 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
- Theo Buhler and Dietmar A. Salamon, Functional Analysis
- Gerald Teschl, Topics in Real and Functional Analysis
- Andrew Lin and Casey Rodriguez, MIT 18.102 Introduction to Functional Analysis
- MathCounterexamples.net, Distance between a point and a hyperplane not reached
- Hamel basis (Wikipedia)
- Haim Brezis, Functional Analysis, Sobolev Spaces and Partial Differential Equations