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.
The rank of a matrix equals the rank of the linear map
Statement
For , the matrix rank of equals the rank of the linear map defined by .
Facts & Assumptions
Given: A matrix and its associated linear map .
Matrix rank is row rank, which equals column rank (Row space, column space, nullspace, row rank, column rank and matrix rank).
Row rank equals column rank (Row rank equals column rank, and both equal the number of pivots).
The rank of a linear map is the dimension of its image (Rank and nullity of a linear map with finite-dimensional domain).
Matrix multiplication is given entrywise by (Rectangular matrix multiplication and the identity matrix , including zero-sized shapes).
Matrix multiplication distributes over addition and is compatible with scalar multiplication (Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication).
Proof
Fact [L5] shows that is linear. By [L4], every value is a linear combination of the columns of , with coefficients , and every such combination is for that coefficient column. Hence .
Taking dimensions and using [L1]–[L3] gives .
Depends on
- Row space, column space, nullspace, row rank, column rank and matrix rank
- Row rank equals column rank, and both equal the number of pivots
- Rank and nullity of a linear map with finite-dimensional domain
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication
Used by
- For an m× n matrix A, rank(A)+dim N(A)=n Corollary
- A matrix has rank at least r exactly when it has a nonzero r-rowed minor Lemma
- If Ax=b has one solution xₚ, then its full solution set is the affine subspace xₚ+N(A) Theorem
- Invertible matrix theorem: invertibility, full pivot rank, RREF I, trivial nullspace and unique solvability are equivalent Theorem
Dependency tree · two levels
21 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- D. Margalit and J. Rabinoff, Interactive Linear Algebra, §3.5 (standard reference, not scraped)