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How to find the inverse of a matrix obtained by cyclically permuting the rows of a known matrix without recomputing it?

When the rows of a matrix are cyclically permuted, its inverse is found by applying the corresponding cyclic permutation to the columns of the original inverse matrix. This avoids complex calculations like Gaussian elimination or adjugate matrices. By recognizing the row rearrangement as a permutation, you can directly reorder the columns of the known inverse to match the new row positions, ensuring the product of the new matrix and its reordered inverse yields the identity matrix.

Conditions

  • The original matrix is invertible.
  • The new matrix is formed by a cyclic permutation of the original matrix's rows.
  • The inverse of the original matrix is already known.

Reasoning, step by step

  1. Identify the row permutation applied to the original matrix to form the new matrix.
  2. Determine the corresponding column permutation that must be applied to the original inverse matrix.
  3. Apply the column permutation to the known inverse matrix.
  4. Verify that the product of the new matrix and the permuted inverse equals the identity matrix.

Example

Given A=[abcdefghi]A = \begin{bmatrix} a & b & c \\ d & e & f \\ g & h & i \end{bmatrix} and its inverse A−1=[a′b′c′d′e′f′g′h′i′]A^{-1} = \begin{bmatrix} a' & b' & c' \\ d' & e' & f' \\ g' & h' & i' \end{bmatrix}, if the new matrix is N=[ghiabcdef]N = \begin{bmatrix} g & h & i \\ a & b & c \\ d & e & f \end{bmatrix}, the rows of AA are permuted as 3, 1, 2. Therefore, the columns of A−1A^{-1} must be permuted as 3, 1, 2, yielding N−1=[c′a′b′f′d′e′i′g′h′]N^{-1} = \begin{bmatrix} c' & a' & b' \\ f' & d' & e' \\ i' & g' & h' \end{bmatrix}.

Common misconceptions

  • Believing that hard calculation using the adjugate matrix or Gaussian elimination is required for every inverse matrix problem.
  • Assuming that having the same set of elements in a candidate matrix automatically makes it the inverse, ignoring the importance of row and column positions.
  • Thinking that when the rows of the original matrix are rearranged, the columns of the inverse matrix remain in their original order.

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