133 - FML 1 - 18.1W e.g. 10.1

checklist
Mark Yourself Question
  1. Grab a piece of paper and try this problem yourself.
  2. When you're done, check the "I have answered this question" box below.
  3. View the solution and report whether you got it right or wrong.
A square n × n matrix AA is said to be diagonalizable if it is similar to a diagonal matrix, i.e. A‾=P ⁣‾ D ⁣‾ P ⁣‾ −1\A = \matP \bct{\D} \matP^{\bct{-1}} .

Rearranging, we have the definition that A‾\A is diagonlizable if D ⁣‾ =P ⁣‾ −1A‾P ⁣‾ \D=\matP^{\bct{-1}} \A \matP is diagonal.

The notation itself should make clear that this is only possible if there is an n×nn\times n matrix P ⁣‾ \matP that is invertible (i.e. ∃  P ⁣‾ −1  :  P ⁣‾ P ⁣‾ −1  =  I ⁣‾ \exist \; \minv{\matP} \; : \; \matP\minv{\matP} \; = \;\I). The question then arises: what is P ⁣‾ \matP and how can we find it?


  • e.g. For an n×nn\times n matrix A‾\A, assume P ⁣‾ \matP is an n×nn\times n invertible matrix, and D ⁣‾ = diag(λ1,λ2,λ3,…,λn)\D = \text{ diag}(\lambda_1 , \lambda_2 , \lambda_3 , \ldots, \lambda_n ). Without assuming anything else, use the definition of diagonalizability (viz. A‾=P ⁣‾ D ⁣‾ P ⁣‾ −1\A = \matP \D \minv{\matP} ) to find a necessary and sufficient condition for defining the matrix P ⁣‾ \matP by completing the following steps:

  1. Right-multiply the definition for diagonalizability by P ⁣‾ \matP:
  2. Let x⃗i{\vx}_{\bco{i}} be the ith\bco{i}^{th} column of the matrix P ⁣‾ \matP, then P ⁣‾ =\matP=
  3. Using your understanding of the nature of matrix multiplication in general, as well as the special case of right-multiplication of a matrix by a diagonal matrix, use the above representation of P ⁣‾ \matP to find column representations of A‾P ⁣‾ \A\matP and P ⁣‾ D ⁣‾ \matP \D.






More Diagonalization Questions: