Linear combination
Definition
Let be a field and let be a vector space over . Let . A linear combination of is a vector of the form where are the coefficients. For an arbitrary family , a linear combination is a sum in which only finitely many coefficients are nonzero.
Remarks
The empty linear combination, with , is the zero vector. The vector is the linear combination of the standard basis vectors of . The set of all linear combinations of a family is its linear span. The matrix–vector product is a linear combination of the columns of , with the entries of as coefficients.
Explanations
L1 human
A linear combination mixes some vectors: scale each one by a number, then add the results. With the vectors and we can make every vector of the plane in this way. Infinite sums are not allowed: only finitely many coefficients can be nonzero.
What this node needs for W2
- edge instance_of in: one example (0 of 1)
- edge not_instance_of in: one non-example that fails one clause (0 of 1)
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To read this node, read these 2 nodes in this order. The foundations come first, and the list ends with this node.
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- Vector space definition
- Linear combination definition
What depends on this
80 nodes use this node, directly or through other nodes.
- Dimension of the dual space 00889YD15K
- Linear map of a matrix 009892PZ30
- Nullity 00K4FK4NBH
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- Finite-dimensional vector space 018W3V3TCT
- Rank of a product of matrices 02FDWY55AS
- Determinant of a linear map 03F5KP94K0
- Parseval's identity 047RFCNVSK
- Eigenvalues are the roots of the characteristic polynomial 053QVHF6N7
- A matrix is diagonalisable iff it has a basis of eigenvectors 056SW1E0AC
- Isomorphism with the coordinate space 059THXBRBB
- Dimension 05G2G0P224
- Matrix of a composition is the product of matrices 05Q1B7MH0M
- Dimension of a direct product 06HN47VPTH
- Grassmann's formula 06HTJNCMPA
- Spectral theorem 06J4PXRG22
- Every spanning set contains a basis 06MRNP4BNT
- Dimension of the annihilator 06Q1EFVH0Q
- Geometric multiplicity 06YSJVR356
- Existence of an eigenvalue over an algebraically closed field 08CJB2NQ92
- Steinitz exchange lemma 08ZDRN6H41
- An injective endomorphism of a finite-dimensional space is surjective 0912EEEVCT
- Linearly independent set of the right size is a basis 096V7H13H9
- Coordinates in an orthonormal basis 09DZ8CD1AK
- Spanning set 0BJKVF90Z6
- Coordinate vector 0BMVBHTWV2
- Characteristic polynomial of a linear map 0BZ81AJNWF
- Determinant is the product of the eigenvalues 0C2F40MEBC
- Classification of complex quadratic forms 0CEX9BRXRZ
- Matrix of the adjoint is the conjugate transpose 0DBTE6HVBD
- Spectral theorem for Hermitian matrices 0DGZBQWZWN
- Dimension of the orthogonal complement 0E2NYPVSP9
- Row space 0EP73HK4C4
- QR decomposition 0ERWG4NXQ9
- Linear independence 0EVHJFTG9Z
- Rank of a matrix 0EWQEYRVA8
- Column space 0F0RPPJ2TT
- Orthonormal basis 0FWJ0VW561
- Eigenvectors with distinct eigenvalues are linearly independent 0HNMFG8F8K
- Linear span 0HR573CHNM
- Rouché–Capelli theorem 0HV1SKWPMS
- Rank of a linear map 0JGB7A6GTS
- An orthonormal family is linearly independent 0JWM6GJNQW
- Determinant of a linear map is the determinant of its matrix 0KMN32XGPF
- Signature of a quadratic form 0KQZYGEJTF
- Change of basis matrix 0KWXYZPJKN
- Every linearly independent set extends to a basis 0M4SXE5WW3
- Change of basis formula 0MQXVZEBMT
- Trace is the sum of the eigenvalues 0NMXDCNNKR
- Matrix of a bilinear form 0P24YW565F
- Principal axis theorem 0PR5P9Z4KM
- Orthogonal complement of the orthogonal complement 0PXN9G4MX1
- Orientation of a real vector space 0Q8BZXVT1Z
- Cayley–Hamilton theorem for linear maps 0QEM68Y0DE
- Dimension of the coordinate space 0QK7FPE6VE
- Dimension of a subspace 0QW084460E
- Dimension of a tensor product 0R84PF4JV7
- A linear map is determined by its values on a basis 0R8X9XA83A
- Algebraic multiplicity 0S7A0EMPF4
- Basis of a tensor product 0SH5VT8QYA
- Change of basis for a bilinear form 0SPYAGE7T0
- Dimension of a quotient space 0SXDQ6TN00
- Row rank equals column rank 0TZAG60ZYT
- Determinant of a family of vectors in a basis 0V94H674MB
- Finite-dimensional spaces are isomorphic iff they have equal dimension 0VJBSK15SN
- Gram–Schmidt process 0VT6P0GNQR
- Basis 0VTQTPQGAV
- Rank–nullity theorem 0W4FSW1TVB
- Dual basis 0W6M3PSD08
- Trace of a linear map is the trace of its matrix 0WCPH5Z7FA
- Sylvester's law of inertia 0WFED3K3DQ
- A subspace of full dimension is the whole space 0WGAXEK99E
- Dimension theorem for vector spaces 0WK0Y8RH1M
- Gram–Schmidt vectors are orthogonal 0WXBGBTA3D
- Existence of an orthonormal basis 0Y85GYEGSF
- Distinct eigenvalues imply diagonalisable 0Y95Z7MT02
- Matrix of a linear map 0YYMY95FWS
- Dimension of the space of linear maps 0Z9N1ER0W0
- Canonical isomorphism with the double dual 0ZJM9D7NPS
- Matrix of the dual map is the transpose 0ZPB3180AY
Sources
What links here
Used by
- Linear independence 0EVHJFTG9Z
- Linear span 0HR573CHNM