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Row reduction
Gaussian elimination, rank and solving Ax = b.
Three row operations (swap, scale, add a multiple of one row to another) bring any matrix to reduced row-echelon form. The number of pivots is the rank; the reduced form of an augmented matrix reads out the solution of a linear system.
Eksempel på arbeid · rref [[1,2,3],[4,5,6],[7,8,9]]
Rref [[1,2,3],[4,5,6],[7,8,9]]
Steg for trinn
- \left[\begin{matrix}1 & 2 & 3\\4 & 5 & 6\\7 & 8 & 9\end{matrix}\right]
Gauss-Jordan elimination.
- \left[\begin{matrix}1 & 2 & 3\\0 & -3 & -6\\7 & 8 & 9\end{matrix}\right]
R2 ← R2 − (4)·R1 to clear column 1.
- \left[\begin{matrix}1 & 2 & 3\\0 & -3 & -6\\0 & -6 & -12\end{matrix}\right]
R3 ← R3 − (7)·R1 to clear column 1.
- \left[\begin{matrix}1 & 2 & 3\\0 & 1 & 2\\0 & -6 & -12\end{matrix}\right]
R2 ← R2 / -3 to make the pivot 1.
- \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & -6 & -12\end{matrix}\right]
R1 ← R1 − (2)·R2 to clear column 2.
- \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & 0 & 0\end{matrix}\right]
R3 ← R3 − (-6)·R2 to clear column 2.
- \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & 0 & 0\end{matrix}\right]
Reduced row-echelon form.
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Hvordan: Row reduction
- Gauss-Jordan elimination.
- R2 ← R2 − (4)·R1 to clear column 1.
- R3 ← R3 − (7)·R1 to clear column 1.
- R2 ← R2 / -3 to make the pivot 1.
- R1 ← R1 − (2)·R2 to clear column 2.
- R3 ← R3 − (-6)·R2 to clear column 2.
- Reduced row-echelon form.
Spørsmål folk stiller
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.
Mer i Linear Algebra
DeterminantsMatrix inverseEigenvalues and eigenvectorsMatrix multiplicationVector spaces, span and linear independenceOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis