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.
A matrix has rank at least exactly when it has a nonzero -rowed minor
Statement
Let and let . Then if and only if some -rowed minor of is nonzero (Submatrices and minors of a rectangular matrix). Equivalently, the rank of is the largest positive size of a nonzero minor when , and it is when every entry is zero (The rank of a matrix equals the rank of the linear map ).
Facts & Assumptions
Given: A real matrix and a natural with .
The rank of a matrix is the dimension of its column space, because row rank equals column rank (Row space, column space, nullspace, row rank, column rank and matrix rank, Row rank equals column rank, and both equal the number of pivots); a list is independent exactly when only the zero coefficient list has zero linear combination (Linear independence: a finite list is independent when forces every , and a subset is independent when every injective finite list into is independent).
A real matrix has rank exactly when it is invertible, and it is invertible exactly when its determinant is nonzero (Invertible matrix theorem: invertibility, full pivot rank, RREF , trivial nullspace and unique solvability are equivalent, A finite square real matrix is invertible if and only if its determinant is nonzero).
Proof
Suppose first that the -minor is nonzero, and write for the corresponding submatrix.
Conversely, suppose . Choose independent columns and form the resulting matrix . Its column rank and hence its row rank are , so its rows span and contain independent rows.
By [L2], is invertible. If a linear combination of the columns of indexed by vanishes, restricting that equality to the rows in gives , hence . Those columns are independent, so .
The submatrix determined by those columns and rows has rank , so [L2] makes its determinant nonzero. This is an -rowed minor of , proving the converse and the equivalence.
Depends on
Submatrices and minors of a rectangular matrix
Row space, column space, nullspace, row rank, column rank and matrix rank
Row rank equals column rank, and both equal the number of pivots
The rank of a matrix equals the rank of the linear map
Invertible matrix theorem: invertibility, full pivot rank, RREF , trivial nullspace and unique solvability are equivalent
A finite square real matrix is invertible if and only if its determinant is nonzero
Linear independence: a finite list is independent when forces every , and a subset is independent when every injective finite list into is independent