Alphabeta Math
DefinitionDefinition: AI-adaptedProof: Not applicablePipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-22
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Orthogonality and the orthogonal complement

Definition

Let V be a real or complex inner-product space. Vectors x,y∈V are orthogonal, written x⊥y, when

⟨x,y⟩=0.

For a subset S⊆V the orthogonal complement of S is

S⊥:={ v∈V:⟨v,s⟩=0 for every s∈S }.

Orthogonality is symmetric. If ⟨x,y⟩=0, then ⟨y,x⟩=⟨x,y⟩‾=0, so x⊥y exactly when y⊥x; in particular the condition defining S⊥ is symmetric in its two arguments.

S⊥ is a linear subspace. Let s∈S and scalars a,b; then ⟨0,s⟩=0, and if u,v∈S⊥ then ⟨au+bv,s⟩=a⟨u,s⟩+b⟨v,s⟩=0 by linearity in the first argument, so au+bv∈S⊥. Thus S⊥ is a linear subspace of V (Linear subspace of a vector space) for every subset S, whether or not S is a subspace. Moreover 0∈S⊥ always, and v∈V lies in {0}⊥ for every v, so {0}⊥=V.

Monotonicity. If S⊆T⊆V, then every vector orthogonal to all of T is orthogonal to all of S, so T⊥⊆S⊥.

Nontriviality of orthogonality. By positive definiteness ⟨v,v⟩=∥v∥2=0 exactly for v=0 (The induced length is a norm), so a vector orthogonal to itself is zero, and {0}⊥=V, V⊥={0}.

Depends on

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