The Observation Theory EncyclopediaFrom TSKAboutBy kindBy chapterBy Lean fileLedgerProvenance

inverse concept

DefinitionThe matrix that undoes a square matrix, so that the inverse times A times x is x. It exists exactly when the columns are independent, which for a two by two matrix means ad minus bc is not zero, and for a symmetric matrix it inverts each eigenvalue. Primer L, equations L.6 and L.15.
ExampleThe matrix with rows (2, 1) and (1, 3) has determinant 5 and inverse with rows (3, -1)/5 and (-1, 2)/5.
BookData Mining as Observation, draft 0.2, commit f3914f0; entry id inverse, kind concept.
Statusno ledger row names this entry. Corrections: none recorded.
Defining equation

Book equation L.6.

Assumptions and scopenone
Prior artnone recorded
Evidencenone
Reviewedsemantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page.

Equation

Book equation L.6.

\[A=\begin{pmatrix}a&b\\ c&d\end{pmatrix},\qquad \det A=ad-bc,\qquad A^{-1}=\frac{1}{ad-bc}\begin{pmatrix}d&-b\\ -c&a\end{pmatrix}.\]

Book equation L.15.

\[x^{\top}Ax=\sum_i\lambda_i\,(v_i\cdot x)^{2},\qquad A^{-1}=\sum_i\lambda_i^{-1}v_iv_i^{\top},\qquad A^{1/2}=\sum_i\lambda_i^{1/2}v_iv_i^{\top}.\]

Conditions

none

Ledger

none

First stated

Primer L section L.3 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix A covers it at length.

Measurements

none

Failures and corrections

none

Invariance envelope

none declared

Machine checked

none

Used in

Data Mining as Observation primer L, chapters 0, 2, 3, 4, 10, 11.

Related

determinant; rank; eigenvalue, eigenvector; matrix.

See also

none

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

← intrinsic dimensioninverted file →