Linear and multilinear algebra; matrix theory Proof
Proof of the formula for coordinates in an orthonormal basis
Builds on Inner productOrthonormal basisOrthonormal familyBasis
Proof
We compute the inner product of with each basis vector.
- Let be a finite-dimensional inner product space over or , with inner product . Let be an orthonormal basis of , so .
- Let . Since is a basis, there are scalars with .
- Fix with . Linearity in the first argument gives .
- By orthonormality, every term with is , and the term with is . So .
- Substituting step 4 into step 2 gives .
- The coordinates of in a basis are unique, so they are the numbers .
Idea
Write in the basis with unknown coefficients. The inner product with kills every term except the one with , because the basis vectors are orthogonal unit vectors. So this inner product is exactly the coefficient .
Reading path
To understand this page from the ground up, read these 12 pages in order. The foundations come first.
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- Vector space definition
- Inner product definition
- Linear combination definition
- Linear subspace definition
- Orthogonal vectors definition
- Linear independence definition
- Linear span definition
- Orthonormal family definition
- Spanning set definition
- Basis definition
- Orthonormal basis definition
- Proof of the formula for coordinates in an orthonormal basis proof
Where this leads
99 pages build on this one.
- Singular value decomposition
- Reflection
- Parseval's identity
- Riesz representation theorem in finite dimensions
- Spectral theorem
- Coordinates in an orthonormal basis
- Least squares
- Matrix of the adjoint is the conjugate transpose
- Spectral theorem for Hermitian matrices
- Dimension of the orthogonal complement
- Polar decomposition
- Orthogonal projection is the closest point
- Classification of isometries of the plane
- Principal axis theorem
- Orthogonal complement of the orthogonal complement
- Orthogonal projection
- A space is the direct sum of a subspace and its orthogonal complement
- Cartan–Dieudonné theorem
- Earthquake location problem
- Non-negative least squares
- Proof of the principal axis theorem from the spectral theorem
- Hellinger–Reissner principle
- Conjugate residual method
- Full waveform inversion
- Local regression
- Geophysical inversion
- Proof of the singular value decomposition from the spectral theorem
- Hilbert–Schmidt expansion theorem
- Minimal residual method
- Toeplitz operator
- Moving horizon estimation
- Céa's lemma
- Bilinear series of a symmetric kernel
- Ordinary least squares
- Proof that the orthogonal projection is the closest point
- Smoothing spline
- Proof of the Riesz representation theorem in finite dimensions
- Finite element method in structural mechanics
- Lax–Milgram theorem
- Proof of Parseval's identity
- Uhlmann's theorem
- Response surface methodology
- Nonlinear regression
- Spectral theorem for compact self-adjoint operators
- Courant–Fischer characterization of characteristic values
- Generalized minimal residual method
- Minimum total potential energy principle
- Proof of the spectral theorem for self-adjoint operators by induction on the dimension
- Data assimilation
- Principle of material frame-indifference
- Proof of the Cartan–Dieudonné theorem by induction on the dimension
- Mixed state
- Purification of a quantum state
- Gauss–Newton algorithm
- Proof that the matrix of the adjoint is the conjugate transpose
- Hilbert space adjoint
- Lidskii's trace formula for integral operators
- Proof that the orthogonal complement of the orthogonal complement is the subspace
- Spectrum of the Laplace–Beltrami operator
- Principal stresses
- Hilbert projection theorem
- Eckart–Young theorem
- Proof of the formula for the dimension of the orthogonal complement
- Korn's inequality
- Ridge regression
- L-curve
- Proof of the polar decomposition from the singular value decomposition
- Biconjugate gradient stabilized method
- Galerkin method
- GNSS positioning
- System identification
- Bernstein's theorem on absolutely convergent Fourier series
- Schmidt decomposition
- Operator monotone function
- Empirical risk minimization
- Isotonic regression
- Projection body
- Mercer's theorem
- Proof of the spectral theorem for Hermitian matrices
- Bra–ket notation
- Principal component analysis
- Hu–Washizu principle
- Inertia tensor and principal axes
- Bidiagonalization
- Ladyzhenskaya–Babuška–Brezzi condition
- Proof that a space is the direct sum of a finite-dimensional subspace and its orthogonal complement
- Levenberg–Marquardt algorithm
- Schur product theorem
- Riesz representation theorem
- Box–Jenkins method
- Singular values of a compact operator
- Lasso
- Householder transformation
- Numerical methods for linear least squares
- Schmidt's singular value decomposition of a kernel
- Polar decomposition of the deformation gradient
- Channel capacity of a quantum channel
- Proof of the classification of isometries of the plane
- Lomb–Scargle periodogram
Further reading
- Web https://linear.axler.net/LADR4e.pdf, Theorem 6.30
About this page
| Tag | NGCNGVNWQ6 |
|---|---|
| URI | m2://NGCNGVNWQ6 |
| Kind | proof |
| Writing | W0 stub |
| MSC | 15A63, 46C05 |
| Proves | Coordinates in an orthonormal basis |
| Uses | Inner product, Orthonormal basis, Orthonormal family, Basis |
| Changed | 4 November 2026 |
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