TEST

HW5/README.md

@@ -4,7 +4,7 @@
 *Include all work as either an m-file script, m-file function, or example code included
 with \`\`\` and document your code in the README.md file*
 
-1. Create a new github repository called 'linear_algebra'. 
+1. Create a new github repository called 'linear_algebra'.
 
     a. Add rcc02007 and pez16103 as collaborators.
 
@@ -13,7 +13,7 @@ with \`\`\` and document your code in the README.md file*
 2. Create an LU-decomposition function called `lu_tridiag.m` that takes 3 vectors as inputs
 and calculates the LU-decomposition of a tridiagonal matrix. The output should be 3
 vectors, the diagonal of the Upper matrix, and the two off-diagonal vectors of the Lower
-and Upper matrices. 
+and Upper matrices.
 
     ```[ud,uo,lo]=lu_tridiag(e,f,g);```
 
@@ -26,7 +26,7 @@ the backslash solver `\`, create an algebraic solution*
 
 4. Test your function on the matrices A3, A4, ..., A10 generated with `test_arrays.m`
 Solving for `b=ones(N,1);` where N is the size of A.  In your `README.md` file, compare
-the norm of the error between your result and the result of AN\b. 
+the norm of the error between your result and the result of AN\b.
 
 ```
 | size of A | norm(error) |
@@ -40,10 +40,10 @@ the norm of the error between your result and the result of AN\b.
 5. In the system shown above, determine the three differential equations for the position
 of masses 1, 2, and 3. Solve for the vibrational modes of the spring-mass system if k1=10
 N/m, k2=k3=20 N/m, and k4=10 N/m. The masses are m1=1 kg, m2=2 kg and m3=4 kg. Determine
-the eigenvalues and natural frequencies. 
+the eigenvalues and natural frequencies.
 
 6. The curvature of a slender column subject to an axial load P (Fig. P13.10) can be
-modeled by 
+modeled by
 
 $\frac{d^{2}y}{dx^{2}} + p^{2} y = 0$
 
@@ -54,7 +54,7 @@ about its neutral axis.
 
 This model can be converted into an eigenvalue problem by
 substituting a centered finite-difference approximation for the second derivative to give
-$\frac{y_{i+1} -2y_{i} + y_{i-1} }{\Delta x^{2}}+ p^{2} y_{i}$ 
+$\frac{y_{i+1} -2y_{i} + y_{i-1} }{\Delta x^{2}}+ p^{2} y_{i}$
 
 where i = a node located at a position along the rod’s interior, and $\Delta x$ = the
 spacing between nodes. This equation can be expressed as $y_{i-1} - (2 - \Delta x^{2}
@@ -66,11 +66,11 @@ Determine the eigenvalues for a 5-segment (4-interior nodes), 6-segment (5-inter
 nodes), and 10-segment (9-interior nodes). Using the modulus and moment of inertia of a
 pole for pole-vaulting (
 [http://people.bath.ac.uk/taf21/sports_whole.htm](http://people.bath.ac.uk/taf21/sports_whole.htm))
-E=76E9 Pa, I=4E-8 m^4, and L= 5m. 
+E=76E9 Pa, I=4E-8 m^4, and L= 5m.
 
 Include a table in the `README.md` that shows the following results:
 What are the largest and smallest eigenvalues for the beam? How many eigenvalues are
-there? 
+there?
 
 ```
 | # of segments | largest | smallest | # of eigenvalues |

extra_credit_1/data.csv

@@ -1,3 +1,8 @@
 radius (cm), angle (degrees)
 0, 0
 2.25, 62
+3.81, 140
+6.35, 116
+.635, 88
+7.62, 12
+1.27, 180 

lecture_01/lecture_01.md

@@ -106,6 +106,5 @@ plot(t,v_analytical,'-',t,v_numerical,'o-')
 
 ![plot of
 velocities](https://github.uconn.edu/rcc02007/ME3255S2017/blob/master/lecture_01/output_10_0.svg)
-<img
-src="https://github.uconn.edu/rcc02007/ME3255S2017/blob/master/lecture_01/output_10_0.svg">
+<img src="https://github.uconn.edu/rcc02007/ME3255S2017/blob/master/lecture_01/output_10_0.svg">
 

lecture_07/lecture_07.ipynb

@@ -1435,22 +1435,23 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Octave",
-   "language": "octave",
-   "name": "octave"
+   "display_name": "Python [conda root]",
+   "language": "python",
+   "name": "conda-root-py"
   },
   "language_info": {
-   "file_extension": ".m",
-   "help_links": [
-    {
-     "text": "MetaKernel Magics",
-     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
-    }
-   ],
-   "mimetype": "text/x-octave",
-   "name": "octave",
-   "version": "0.19.14"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

lecture_07/mod_secant.m

@@ -14,13 +14,13 @@
 %   ea = approximate relative error (%)
 %   iter = number of iterations
 if nargin<3,error('at least 3 input arguments required'),end
-if nargin<4 || isempty(es),es=0.0001;end
+if nargin<4 || isempty(es),es=0.0001;end	
 if nargin<5 || isempty(maxit),maxit=50;end
-iter = 0;
+iter = 0
 while (1)
   xrold = xr;
   dfunc=(func(xr+dx)-func(xr))./dx;
-  xr = xr - func(xr)/dfunc;
+  xr = xr - func(xr)/dfunc; %calculate as an approximation
   iter = iter + 1;
   if xr ~= 0 
     ea = abs((xr - xrold)/xr) * 100;

lecture_10/GaussNaive.m

@@ -13,7 +13,9 @@
 % forward elimination
 for k = 1:n-1
   for i = k+1:n
-    factor = Aug(i,k)/Aug(k,k);
+    factor = Aug(i,k)/Aug(k,k); 
+    %this is naive because if a coefficient in the matrix is zero
+    %then divide by zero errot.
     Aug(i,k:nb) = Aug(i,k:nb)-factor*Aug(k,k:nb);
   end
 end