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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.

Przykład pracownika · rref [[1,2,3],[4,5,6],[7,8,9]]

Linear-Algebra · Row-Reduction

Rref [[1,2,3],[4,5,6],[7,8,9]]

\left[\begin{matrix}1 & 2 & 3\\4 & 5 & 6\\7 & 8 & 9\end{matrix}\right]

Krok po kroku

  1. \left[\begin{matrix}1 & 2 & 3\\4 & 5 & 6\\7 & 8 & 9\end{matrix}\right]

    Gauss-Jordan elimination.

  2. \left[\begin{matrix}1 & 2 & 3\\0 & -3 & -6\\7 & 8 & 9\end{matrix}\right]

    R2 ← R2 − (4)·R1 to clear column 1.

  3. \left[\begin{matrix}1 & 2 & 3\\0 & -3 & -6\\0 & -6 & -12\end{matrix}\right]

    R3 ← R3 − (7)·R1 to clear column 1.

  4. \left[\begin{matrix}1 & 2 & 3\\0 & 1 & 2\\0 & -6 & -12\end{matrix}\right]

    R2 ← R2 / -3 to make the pivot 1.

  5. \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & -6 & -12\end{matrix}\right]

    R1 ← R1 − (2)·R2 to clear column 2.

  6. \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & 0 & 0\end{matrix}\right]

    R3 ← R3 − (-6)·R2 to clear column 2.

  7. \left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & 0 & 0\end{matrix}\right]

    Reduced row-echelon form.

Odkryj odpowiedź
\left[\begin{matrix}1 & 0 & -1\\0 & 1 & 2\\0 & 0 & 0\end{matrix}\right]

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Jak to zrobić?: Row reduction

  1. Gauss-Jordan elimination.
  2. R2 ← R2 − (4)·R1 to clear column 1.
  3. R3 ← R3 − (7)·R1 to clear column 1.
  4. R2 ← R2 / -3 to make the pivot 1.
  5. R1 ← R1 − (2)·R2 to clear column 2.
  6. R3 ← R3 − (-6)·R2 to clear column 2.
  7. Reduced row-echelon form.

Pytania, które ludzie zadają

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.

Więcej w Linear Algebra

DeterminantsMatrix inverseEigenvalues and eigenvectorsMatrix multiplicationVector spaces, span and linear independenceOrthogonality, projections and least squaresDiagonalisation and matrix powersLinear transformations and change of basis