diff --git a/mp5/Project_5_Maggioni_Claudio.pdf b/mp5/Project_5_Maggioni_Claudio.pdf
index 3f49ab5..dc851bf 100644
Binary files a/mp5/Project_5_Maggioni_Claudio.pdf and b/mp5/Project_5_Maggioni_Claudio.pdf differ
diff --git a/mp5/Project_5_Maggioni_Claudio.tex b/mp5/Project_5_Maggioni_Claudio.tex
index 29765e4..27c6802 100644
--- a/mp5/Project_5_Maggioni_Claudio.tex
+++ b/mp5/Project_5_Maggioni_Claudio.tex
@@ -72,7 +72,6 @@ second derivative we have:
 since $A$ is positive definite. Therefore, we can say that the absolute minima
 of $f(x)$ is the solution for $Ax = b$.
 
-
 \section{Conjugate Gradient [40 points]}
 \subsection{ Write a function for the conjugate gradient solver
 \texttt{[x,rvec]=myCG(A,b,x0,max\_itr,tol)}, where \texttt{x}
@@ -98,8 +97,10 @@ The plot of the squared residual 2-norms over all iterations can be found in Fig
 condition number and convergence rate.}
 
 The eigenvalues of A can be found in figure
-\ref{fig:plot2}. The condition number for matrix $A$ according to \texttt{rcond(...)} is $\approx 3.2720 \cdot 10^7$, which is very low without sitting in the denormalized range (i.e. $< \text{eps}$) and thus very good for the Conjugate Gradient algorithm.
-This well conditioning is also reflected in the eigenvalue plot, which shows a not so
+\ref{fig:plot2}. The condition number for matrix $A$ according to \texttt{rcond(...)} is $\approx 3.2720 \cdot 10^7$,
+which is very low without sitting in the denormalized range (i.e. $< \text{eps}$) and thus very good for the Conjugate Gradient algorithm.
+This well conditioning is also
+reflected in the eigenvalue plot, which shows a not so
 drastic increase of the first eigenvalues ordered in increasing order.
 
 \begin{figure}[h]
@@ -118,9 +119,14 @@ Matlab documentation). Solve the system with both solvers using
 $max\_iter=200$ $tol= 10^{-6}$. Plot the convergence (residual
 vs iteration) of each solver and display the original and final
 deblurred image.}
+
+Plots already rendered.
+
 \subsection{ When would \texttt{pcg} be worth the added computational cost?
 What about if you are debluring lots of images with the same
 blur operator?}
 
+\textit{pcg} better for many, myCG better for one thanks to cost of ichol.
+
 
 \end{document}
diff --git a/mp5/Project_5_Maggioni_Claudio/code_template.asv b/mp5/Project_5_Maggioni_Claudio/code_template.asv
deleted file mode 100644
index 3c7199f..0000000
--- a/mp5/Project_5_Maggioni_Claudio/code_template.asv
+++ /dev/null
@@ -1,41 +0,0 @@
-close all;
-clear; clc;
-
-%% Load Default Img Data
-load('blur_data/B.mat');
-B=double(B);
-load('blur_data/A.mat');
-A=double(A);
-ciao = A;
-
-% Show Image
-figure
-im_l=min(min(B));
-im_u=max(max(B));
-imshow(B,[im_l,im_u])
-title('Blured Image')
-
-% Vectorize the image (row by row)
-b=B';
-b=b(:);
-
-%IL = ichol(A, struct('type', 'nofill', 'diagcomp', 0));
-y = IL \ b;
-x0 = IL' \ y;
-
-[x, rvec] = myCG(A, b, diag(IL), 200, 1e-6);
-semilogy(rvec);
-
-[X2,flag,~,~,rvec2] = pcg(A, b, 1e-6, 200);
-
-
-
-%% Validate Test values
-load('test_data/A_test.mat');
-load('test_data/x_test_exact.mat');
-load('test_data/b_test.mat');
-
-%res=||x^*-A^{-1}b||
-res=x_test_exact-inv(A_test)*b_test;
-norm(res);
-%(Now do it with your CG and Matlab's PCG routine!!!)