Finished all chapters and definitions. I need to add subsections and see if there's any theorems or definitions in the appendicies that are worth adding to this as well.
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@@ -3,7 +3,7 @@
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\begin{remark}
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\hfill\\
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Because the determinant of the $n \times n$ matrix is $1$, we can interpret \autoref{Remark 4.1} as the following facts about the determinants of elementary matrices.
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\begin{enumerate}
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\item If $E$ is an elementary matrix obtained by interchanging any two rows of $I$, then $\det(E) = -1$.
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\item If $E$ is an elementary matrix obtained by multiplying some row of $I$ by the nonzero scalar $k$, then $\det(E) = k$.
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@@ -28,10 +28,10 @@
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\begin{theorem}[\textbf{Cramer's Rule}]
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\hfill\\
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Let $Ax = b$ be the matrix form of a system of $n$ linear equations in $n$ unknowns, where $x = (x_1, x_2, \dots, x_n)^t$. If $\det(A) \neq 0$, then this system has a unique solution, and for each $k$ ($k = 1, 2, \dots, n$),
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Let $Ax = b$ be the matrix form of a system of $n$ linear equations in $n$ unknowns, where $x = (x_1, x_2, \dots, x_n)^t$. If $\det(A) \neq 0$, then this system has a unique solution, and for each $k$ ($k = 1, 2, \dots, n$),
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\[x_k = \frac{\det(M_k)}{\det(A)},\]
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where $M_k$ is the $n \times n$ matrix obtained from $A$ by replacing column $k$ of $A$ by $b$.
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\end{theorem}
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@@ -68,14 +68,14 @@
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\begin{definition}
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\hfill\\
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A matrix of the form
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\[\begin{pmatrix}
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1 & c_0 & c_0^2 & \dots & c_0^n \\
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1 & c_1 & c_1^2 & \dots & c_1^n \\
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\vdots & \vdots & \vdots & &\vdots \\
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1 & c_n & c_n^2 & \dots & c_n^n
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\end{pmatrix}\]
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1 & c_0 & c_0^2 & \dots & c_0^n \\
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1 & c_1 & c_1^2 & \dots & c_1^n \\
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\vdots & \vdots & \vdots & & \vdots \\
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1 & c_n & c_n^2 & \dots & c_n^n
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\end{pmatrix}\]
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is called a \textbf{Vandermonde matrix}.
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\end{definition}
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@@ -92,13 +92,13 @@
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\begin{definition}
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\hfill\\
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Let $y_1, y_2, \dots, y_n$ be linearly independent function in $\C^\infty$. For each $y \in \C^\infty$, define $T(y) \in \C^\infty$ by
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\[[T(y)](t) = \det\begin{pmatrix}
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y(t) & y_1(t) & y_2(t) & \dots & y_n(t) \\
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y'(t) & y'_1(t) & y'_2(t) & \dots & y'_n(t) \\
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\vdots & \vdots & \vdots & &\vdots \\
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y^{(n)}(t) & y_1^{(n)}(t) & y_2^{(n)}(t) & \dots & y_n^{(n)}(t)
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\end{pmatrix}\]
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y(t) & y_1(t) & y_2(t) & \dots & y_n(t) \\
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y'(t) & y'_1(t) & y'_2(t) & \dots & y'_n(t) \\
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\vdots & \vdots & \vdots & & \vdots \\
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y^{(n)}(t) & y_1^{(n)}(t) & y_2^{(n)}(t) & \dots & y_n^{(n)}(t)
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\end{pmatrix}\]
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The preceding determinant is called the \textbf{Wronskian} of $y, y_1, \dots, y_n$.
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\end{definition}
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\end{definition}
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