Merge pull request #35 from bowenszhu/patch-2

Fix typo in lecture4/dynamical_systems.html

Author: ChrisRackauckas

lecture4/dynamical_systems.html

@@ -793,7 +793,7 @@ <h2>Multivariable Systems</h2>
 <p class="math">\[
 x_{n+1} = A x_n
 \]</p>
-<p>The easiest way to analyze a multidimensional system is to turn it into a bunch of single dimension systems. To do this, assume that <span class="math">$A$</span> is diagonal. This means that there exists a diagonalization <span class="math">$A =P^{-1}DP$</span> where <span class="math">$P$</span> is the matrix of eigenvectors and <span class="math">$D$</span> is the diagonal matrix of eigenvalues. We can then decompose the system as follows:</p>
+<p>The easiest way to analyze a multidimensional system is to turn it into a bunch of single dimension systems. To do this, assume that <span class="math">$A$</span> is diagonalizable. This means that there exists a diagonalization <span class="math">$A =P^{-1}DP$</span> where <span class="math">$P$</span> is the matrix of eigenvectors and <span class="math">$D$</span> is the diagonal matrix of eigenvalues. We can then decompose the system as follows:</p>
 <p class="math">\[
 Px_{n+1} = DPx_n
 \]</p>

lecture4/dynamical_systems.jmd

@@ -209,7 +209,7 @@ The linear multidimensional discrete dynamical system is:
 $$x_{n+1} = A x_n$$
 
 The easiest way to analyze a multidimensional system is to turn it into a bunch
-of single dimension systems. To do this, assume that $A$ is diagonalizable (or nondefective). This means
+of single dimension systems. To do this, assume that $A$ is diagonalizable. This means
 that there exists a diagonalization $A =P^{-1}DP$ where $P$ is the matrix of
 eigenvectors and $D$ is the diagonal matrix of eigenvalues. We can then decompose
 the system as follows: