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+#+TITLE: Homework 7
+#+AUTHOR: Elizabeth Hunt
+#+LATEX_HEADER: \notindent \notag  \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry}
+#+LATEX: \setlength\parindent{0pt}
+#+OPTIONS: toc:nil
+
+* Question One
+See ~UTEST(eigen, dominant_eigenvalue)~ in ~test/eigen.t.c~ and the entry
+~Eigen-Adjacent -> dominant_eigenvalue~ in the LIZFCM API documentation.
+* Question Two
+See ~UTEST(eigen, leslie_matrix_dominant_eigenvalue)~ in ~test/eigen.t.c~
+and the entry ~Eigen-Adjacent -> leslie_matrix~ in the LIZFCM API
+documentation.
+* Question Three
+See ~UTEST(eigen, least_dominant_eigenvalue)~ in ~test/eigen.t.c~ which
+finds the least dominant eigenvalue on the matrix:
+
+\begin{bmatrix}
+2 & 2 & 4 \\
+1 & 4 & 7 \\
+0 & 2 & 6 
+\end{bmatrix}
+
+which has eigenvalues: $5 + \sqrt{17}, 2, 5 - \sqrt{17}$ and should thus produce $5 - \sqrt{17}$.
+
+See also the entry ~Eigen-Adjacent -> least_dominant_eigenvalue~ in the LIZFCM API
+documentation.
+* Question Four
+See ~UTEST(eigen, shifted_eigenvalue)~ in ~test/eigen.t.c~ which
+finds the least dominant eigenvalue on the matrix:
+
+\begin{bmatrix}
+2 & 2 & 4 \\
+1 & 4 & 7 \\
+0 & 2 & 6 
+\end{bmatrix}
+
+which has eigenvalues: $5 + \sqrt{17}, 2, 5 - \sqrt{17}$ and should thus produce $2.0$.
+
+With the initial guess: $[0.5, 1.0, 0.75]$.
+
+See also the entry ~Eigen-Adjacent -> shift_inverse_power_eigenvalue~ in the LIZFCM API
+documentation.
+* Question Five
+See ~UTEST(eigen, partition_find_eigenvalues)~ in ~test/eigen.t.c~ which
+finds the eigenvalues in a partition of 10 on the matrix:
+
+\begin{bmatrix}
+2 & 2 & 4 \\
+1 & 4 & 7 \\
+0 & 2 & 6 
+\end{bmatrix}
+
+which has eigenvalues: $5 + \sqrt{17}, 2, 5 - \sqrt{17}$, and should produce all three from
+the partitions when given the guesses $[0.5, 1.0, 0.75]$ from the questions above.
+
+See also the entry ~Eigen-Adjacent -> partition_find_eigenvalues~ in the LIZFCM API
+documentation.
+
+* Question Six
+Consider we have the results of two methods developed in this homework: ~least_dominant_eigenvalue~, and ~dominant_eigenvalue~
+into ~lambda_0~, ~lambda_n~, respectively. Also assume that we have the method implemented as we've introduced,
+~shift_inverse_power_eigenvalue~.
+
+Then, we begin at the midpoint of ~lambda_0~ and ~lambda_n~, and compute the
+~new_lambda = shift_inverse_power_eigenvalue~
+with a shift at the midpoint, and some given initial guess.
+
+1. If the result is equal (or within some tolerance) to ~lambda_n~ then the closest eigenvalue to the midpoint
+   is still the dominant eigenvalue, and thus the next most dominant will be on the left. Set ~lambda_n~
+   to the midpoint and reiterate.
+2. If the result is greater or equal to ~lambda_0~ we know an eigenvalue of greater or equal magnitude
+   exists on the right. So, we set ~lambda_0~ to this eigenvalue associated with the midpoint, and
+   re-iterate.
+3. Continue re-iterating until we hit some given maximum number of iterations. Finally we will return
+   ~new_lambda~.