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Vector spaces, span and linear independence
The axioms, spanning sets, independence, basis and dimension.
A vector space is a set closed under addition and scaling. Vectors are independent when no combination cancels; a basis is an independent spanning set, and every basis has the same size: the dimension. Picture it: two vectors spanning a plane unless they are parallel, the 3D view of the column vectors. Think it: rank is the dimension of the column space; rank + nullity = number of columns.
Разобранный пример · rank of [[1,2],[2,4]]
Шаг за шагом
- \left[\begin{matrix}1 & 2\\2 & 4\end{matrix}\right]
Row-reduce; the rank is the number of non-zero rows (pivots).
- \left[\begin{matrix}1 & 2\\0 & 0\end{matrix}\right]
R2 ← R2 − (2)·R1 to clear column 1.
- \operatorname{rank} A = 1
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Как: Vector spaces, span and linear independence
- Put the vectors as columns of a matrix and row-reduce.
- Pivot columns are a basis of the span; the number of pivots is the rank.
- Free columns give the null space: the dependencies among the vectors.
Вопросы, которые люди задают
What does a determinant mean geometrically?
It is the factor by which the matrix scales area (2×2) or volume (3×3), with a negative sign if orientation flips. Zero means the matrix flattens space and cannot be undone.
What is an eigenvector?
A direction the matrix does not turn: it only stretches it by the eigenvalue. Along eigenvectors a complicated matrix acts like multiplication by a number.
Why is matrix multiplication not commutative?
Because a matrix is a transformation and AB means "do B, then A". Rotating then reflecting is not the same as reflecting then rotating.
Ещё в разделе Linear Algebra
DeterminantsMatrix inverseEigenvalues and eigenvectorsMatrix multiplicationRow reductionOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis