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Riesz Representation Theorem (Hilbert)

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Dual Spaces and Bounded Linear FunctionalsOrthogonality in Hilbert Spaces+1 more→→Bessel's Inequality and Parseval's Identity
hilbert-spaces duality

Core Idea

Every bounded linear functional f on a Hilbert space H has the form f(x) = ⟨x, y⟩ for a unique y ∈ H. This natural isomorphism H ≅ H* is special to Hilbert spaces and fails in general Banach spaces.

Explainer

From your study of dual spaces and bounded functionals, you know that H* — the dual of a Hilbert space H — is the space of all bounded linear maps f: H → ℝ (or ℂ). These are the "measurements" you can make: each f takes a vector and returns a number, linearly and continuously. The abstract question is: what do all possible bounded functionals look like? The Riesz Representation Theorem answers this completely — every bounded functional is just an inner product with a fixed vector.

The geometric intuition comes from your understanding of orthogonality in Hilbert spaces. A bounded linear functional f: H → ℝ has a kernel — the closed subspace of all x with f(x) = 0. If f is not identically zero, its kernel has a one-dimensional orthogonal complement. Any vector y in that complement, appropriately normalized, satisfies f(x) = ⟨x, y⟩ for all x. In other words, "measuring x with f" is equivalent to "projecting x onto the direction y." The inner product already computes the projection, so inner products and bounded functionals are the same thing.

More carefully: fix a nonzero bounded functional f. The kernel ker(f) is a closed subspace, so by your orthogonal decomposition results, H = ker(f) ⊕ ker(f)^⊥. The orthogonal complement is one-dimensional — pick any unit vector z in it. Every x ∈ H decomposes as x = (x − f(x)/f(z) · z) + f(x)/f(z) · z. The first part lies in ker(f), the second is a scalar multiple of z. Setting y = f(z)·z̄ (conjugated in the complex case), you get f(x) = ⟨x, y⟩. Uniqueness follows because if ⟨x, y⟩ = ⟨x, y'⟩ for all x, then y = y'.

The consequence is the isomorphism H ≅ H*: the map y ↦ ⟨·, y⟩ is a bijection from H onto H*. This is why Hilbert spaces are called self-dual — a Hilbert space "knows" its own dual. This fails in general Banach spaces: the dual of Lp is Lq (with 1/p + 1/q = 1), which is a different space unless p = 2. The inner product is the special structure that collapses this distinction. For applications — including quantum mechanics, where states live in L² and observables are bounded functionals — this self-duality is indispensable: every observable corresponds to a unique state vector, and vice versa.

Practice Questions 5 questions

Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Introduction to Exponents → Order of Operations → Integer Order of Operations → Variable Expressions → The Distributive Property → Variables and Expressions Review → Introduction to Polynomials → Adding and Subtracting Polynomials → Multiplying Polynomials → Factorial → Permutations → Combinations → Counting Principles: Addition and Multiplication Rules → Introduction to Graph Theory → Propositional Logic Foundations → Logical Equivalences → De Morgan's Laws → Negation of Quantified Statements → Proof by Contradiction → Topological Spaces: Definition and Examples → Open Sets in Topological Spaces → Neighborhoods and Open Sets → Open Sets in Topological Spaces → Basis for a Topology → Metric Topology → Completeness in Metric Spaces → Banach Spaces → Bounded Linear Operators → The Operator Norm → Dual Spaces and Bounded Linear Functionals → Weak Convergence → Weak* Convergence → Reflexive Spaces → Riesz Representation Theorem (Hilbert)

Longest path: 86 steps · 433 total prerequisite topics

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