Linear and multilinear algebra; matrix theory Proof
Proof that the matrix of a composition is the product of the matrices
Builds on Matrix of a linear mapMatrix multiplicationComposition of linear mapsLinear mapBasis
Proof
We compute the image of each basis vector of under in two steps.
- Write , and for the bases of , and .
- Let , an matrix. By the definition of the matrix of a linear map, for .
- Let , an matrix. Then for .
- The composition is linear: for and , since and are linear.
- Fix with . By step 2 and the linearity of , .
- By step 3, this equals .
- By the definition of the matrix product, is the entry of .
- The coordinates of a vector in the basis are unique. So, by step 6, entry of is .
- By steps 7 and 8, the two matrices have the same entries. Hence .
Idea
Column of a matrix records where the -th basis vector goes. Pushing first through and then through produces exactly the sum that defines the entry of the product. This is why matrix multiplication is defined the way it is.
Reading path
To understand this page from the ground up, read these 6 pages in order. The foundations come first.
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- no evidence level
- Basis concept
- Composition of linear maps concept
- Linear map concept
- Matrix multiplication concept
- Matrix of a linear map concept
- Proof that the matrix of a composition is the product of the matrices proof
Where this leads
94 pages build on this one.
- Singular value decomposition
- Lotka–Volterra equations
- Triangularisation
- Chain rule for Jacobian matrices
- Spectral theorem for normal matrices
- Eigenvalues are the roots of the characteristic polynomial
- Matrix of a composition is the product of matrices
- Spectral theorem
- Existence of an eigenvalue over an algebraically closed field
- Linearisation at an equilibrium
- Determinant of a matrix exponential
- Hyperbolic equilibrium point
- Spectral theorem for Hermitian matrices
- Polar decomposition
- Classification of equilibria of planar linear systems
- Principal axis theorem
- Cayley–Hamilton theorem for linear maps
- Hartman–Grobman theorem
- Pendulum equation
- Stability of linear systems via eigenvalues
- Sylvester's criterion
- Decomposition into generalised eigenspaces
- Spectral theorem for unbounded self-adjoint operators
- Routh–Hurwitz theorem
- Proof of the principal axis theorem from the spectral theorem
- Proof of the singular value decomposition from the spectral theorem
- Quantum Zeno effect
- Hilbert–Schmidt expansion theorem
- Anharmonic oscillator
- Proof of Sylvester's criterion by induction and completing the square
- Volterra's principle
- Bilinear series of a symmetric kernel
- Uhlmann's theorem
- Spectral theorem for compact self-adjoint operators
- Courant–Fischer characterization of characteristic values
- Proof of the spectral theorem for self-adjoint operators by induction on the dimension
- Principle of material frame-indifference
- Mixed state
- Melnikov method
- Euler's elastica
- Purification of a quantum state
- Gleason's theorem
- Proof of the eigenvalue criteria for stability of linear systems via the Jordan normal form
- Inverted pendulum
- Stone's theorem on one-parameter unitary groups
- Lidskii's trace formula for integral operators
- Hamiltonian operator
- Second quantization
- Proof of the decomposition into generalised eigenspaces
- Spectrum of the Laplace–Beltrami operator
- Principal stresses
- Furuta pendulum
- Wick rotation
- Eckart–Young theorem
- Proof of the existence of an eigenvalue over an algebraically closed field
- Strang splitting
- Lie–Trotter product formula
- Routh–Hurwitz stability criterion
- Kharitonov's theorem
- Proof of the polar decomposition from the singular value decomposition
- Born rule
- Borel functional calculus
- Schmidt decomposition
- Operator monotone function
- Proof of the spectral theorem for normal matrices by deflation
- Mercer's theorem
- Proof of the spectral theorem for Hermitian matrices
- Proof of triangularisation by induction on the size
- Anharmonic oscillator
- Generalized Lotka–Volterra equation
- Quantum computing
- Wave function collapse
- Principal component analysis
- Proof of the Cayley–Hamilton theorem for linear maps
- Inertia tensor and principal axes
- Bidiagonalization
- Duffing equation
- Kapitza's pendulum
- Liénard–Chipart criterion
- Schur product theorem
- Singular values of a compact operator
- Spherical pendulum
- Proof of the chain rule for Jacobian matrices
- Observable
- Schmidt's singular value decomposition of a kernel
- Proof that the determinant of a matrix exponential is the exponential of the trace
- Proof that the eigenvalues are the roots of the characteristic polynomial
- Van der Pol oscillator
- Polar decomposition of the deformation gradient
- Spectral theorem for bounded normal operators
- Hidden-variable theory
- Channel capacity of a quantum channel
- Gain scheduling
- Normal form
Further reading
About this page
| Tag | 84XDSB1RJ7 |
|---|---|
| URI | m2://84XDSB1RJ7 |
| Kind | proof |
| Writing | W0 stub |
| MSC | 15A04 |
| Proves | Matrix of a composition is the product of matrices |
| Uses | Matrix of a linear map, Matrix multiplication, Composition of linear maps, Linear map, Basis |
| Changed | 4 November 2026 |
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