MathlibAnnex.Matrix.existsUnique_factor_of_orientedMaximalMinorsProportional
theorem
Recovers a square factor and its determinant from proportional determinants of every ordered row tuple.
Statement
Let be -by- matrices over a field and let . For an ordered tuple , let be the selected square matrices, retaining the order and any repetitions in . Assume
Suppose one tuple satisfies . There is a unique square matrix with and .
Assumptions
The coefficient system is any field; is any set, without an order or finiteness assumption. The scalar is nonzero, proportionality holds for all ordered tuples, and the same chosen tuple supplies the nonzero minor. The case is included.
Conclusion
Writing and , the factor is
It satisfies and , hence is invertible. The uniqueness already follows from .
Notes
If has a linear order, proportionality of the increasing-row minor families implies the ordered-tuple hypothesis: sorting an injective tuple introduces the same permutation sign for both matrices, while a repeated row makes both determinants zero. This adapter is separate from the theorem’s order-free assumptions. A nonzero selected minor also implies that multiplication by on column vectors is injective: restrict to the selected rows and multiply by .
Proof route
Use the same nonzero selected minor to compare every row’s coordinates by Cramer’s rule. The coordinate equality supplies the global factorization; the selected square equation supplies its determinant and uniqueness.
Proof steps
Proportionality at gives . Since and are nonzero in a field, . Thus both and are invertible. Define ; immediately .
Fix any row index and write for the corresponding rows. For every position , replace the th entry of by . The hypothesis applies even if this creates a repeated entry. The selected matrices are and , so Cramer’s row formula gives
Cancellation of proves .
Multiply the last row identity on the right by :
It holds for every , hence . Taking determinants in the selected equation gives ; canceling the nonzero factor yields .
If another matrix satisfies , selecting the same rows gives . Multiplication by forces . This proves uniqueness even before imposing the determinant condition.
To pass from increasing minors to arbitrary tuples when an order on is available, write an injective tuple as an increasing enumeration composed with a permutation . Both determinants gain the factor , so their proportionality is unchanged. A noninjective tuple has two equal rows and both determinants vanish. For , there is one empty tuple, both determinants are , and the hypothesis reads , forcing ; the unique empty matrix is the required factor.
Main citations
- Exact
declaration and its source —
MathlibAnnex.Matrix.existsUnique_factor_of_orientedMaximalMinorsProportional - Cramer’s
row-replacement coordinate formula —
MathlibAnnex.Matrix.det_smul_vecMul_nonsingInv_eq_updateRowDet - A
nonzero minor gives injectivity on column vectors —
MathlibAnnex.Matrix.mulVec_injective_of_maximalMinor_ne_zero - An
oriented minor from a row tuple —
MathlibAnnex.Matrix.orientedMaximalMinor - Proportionality
for all ordered row tuples —
MathlibAnnex.Matrix.OrientedMaximalMinorsProportional - Proportionality
for increasing-row minors —
MathlibAnnex.Matrix.MaximalMinorsProportional - Agreement
of increasing and oriented minors —
MathlibAnnex.Matrix.orientedMaximalMinor_eq_of_orderEmbedding - The
factor from the chosen square chart —
MathlibAnnex.Matrix.chartFactor - Changing
a tuple entry replaces the selected row —
MathlibAnnex.Matrix.submatrix_update_rowTuple - The
selected matrices satisfy the factor equation —
MathlibAnnex.Matrix.selectedSubmatrix_mul_chartFactor - The
chart factor has the prescribed determinant —
MathlibAnnex.Matrix.det_chartFactor - Cramer
comparison of arbitrary row coordinates —
MathlibAnnex.Matrix.rowCoordinates_eq_of_orientedMaximalMinorsProportional - Row-coordinate
comparison gives the global factor equation —
MathlibAnnex.Matrix.mul_chartFactor_eq_of_orientedMaximalMinorsProportional - Uniqueness
from the same chosen chart —
MathlibAnnex.Matrix.chartFactor_unique - A
row permutation changes both minors by its sign —
MathlibAnnex.Matrix.orientedMaximalMinor_permute - Repeated
rows give a zero determinant —
MathlibAnnex.Matrix.orientedMaximalMinor_eq_zero_of_not_injective - Sorting
an injective row tuple —
MathlibAnnex.Matrix.exists_orderEmbedding_perm_of_injective - Transfer
from increasing minors to oriented minors —
MathlibAnnex.Matrix.orientedMaximalMinorsProportional_of_maximalMinorsProportional - The
empty-dimensional scale is one —
MathlibAnnex.Matrix.scale_eq_one_of_orientedMaximalMinorsProportional_zero
Lean source signature (exact)
theorem existsUnique_factor_of_orientedMaximalMinorsProportional
{n : ℕ} {ι : Type u} {K : Type v} [Field K]
(source target : _root_.Matrix ι (Fin n) K) (scale : K)
(hscale : scale ≠ 0)
(hminor : OrientedMaximalMinorsProportional source target scale)
(rows : Fin n → ι)
(htarget : orientedMaximalMinor target rows ≠ 0) :
∃! L : _root_.Matrix (Fin n) (Fin n) K,
target * L = source ∧ L.det = scale
| In the source | Mathematical meaning |
|---|---|
{n : ℕ} {ι : Type u} {K : Type v} [Field K] |
, any row set and any coefficient field. No order or finiteness of is assumed. |
(source target : root.Matrix ι (Fin n) K) (scale :
K) |
The two matrices
and scalar
,
respectively; thus target * L = source means
. |
(hscale : scale ≠ 0) |
The hypothesis . |
(hminor : OrientedMaximalMinorsProportional source target
scale) |
For every ordered tuple , . The cited definition retains the tuple order, including repetitions. |
| In the source | Mathematical meaning |
|---|---|
(rows : Fin n → ι) (htarget : orientedMaximalMinor target rows
≠ 0) |
The chosen tuple and the hypothesis . The same chosen rows form and . |
∃! L : root.Matrix (Fin n) (Fin n) K, target * L =
source ∧ L.det = scale |
There is exactly one square matrix satisfying both and . Its formula in the conclusion is . The case is included. |
Exact surrounding binder context (separate excerpt)
noncomputable section
open scoped Matrix
namespace MathlibAnnex
namespace Matrix
universe u v
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