Line 278: Line 278:
 
   1
 
   1
 
  \end{bmatrix}</math>  
 
  \end{bmatrix}</math>  
 +
 +
  
 
Find the minimum length vector <math>x^{(\ast)}</math> that minimizes <math>\| Ax -b\|^{2}_2</math>  
 
Find the minimum length vector <math>x^{(\ast)}</math> that minimizes <math>\| Ax -b\|^{2}_2</math>  
Line 350: Line 352:
  
 
<math>x^{(\ast)}=A^{\dagger}b</math>  
 
<math>x^{(\ast)}=A^{\dagger}b</math>  
 +
 +
Since <math>A = BC = \begin{bmatrix}
 +
  1 & 0  \\
 +
  0 & 1  \\
 +
  0 & -1
 +
\end{bmatrix} \begin{bmatrix}
 +
  1 & 0 &-1  \\
 +
  0 & 1 & 0 
 +
\end{bmatrix}</math>
 +
 +
<math>B^{\dagger} = (B^T B)^{-1}B^T = \begin{bmatrix}
 +
  1 & 0 \\
 +
  0 & 2 
 +
\end{bmatrix}^{-1} \begin{bmatrix}
 +
  1 & 0 & 0  \\
 +
  0 & 1 & -1 
 +
\end{bmatrix} = \begin{bmatrix}
 +
  1 & 0 & 0  \\
 +
  0 & \frac{1}{2} & -\frac{1}{2} 
 +
\end{bmatrix}</math>
 +
 +
      <math>C^{\dagger} = C^T(CC^T)^{-1} =\begin{bmatrix}
 +
  1 & 0  \\
 +
  0 & 1  \\
 +
  -1 & 0
 +
\end{bmatrix} \begin{bmatrix}
 +
  2 & 0 \\
 +
  0 & 1 
 +
\end{bmatrix}^{-1} = \begin{bmatrix}
 +
  \frac{1}{2} & 0  \\
 +
  0 & 1  \\
 +
  -\frac{1}{2} & 0
 +
\end{bmatrix} </math>
 +
 +
      <math>A^{\dagger} = C^{\dagger}B^{\dagger} =\begin{bmatrix}
 +
  \frac{1}{2} & 0  \\
 +
  0 & 1  \\
 +
  -\frac{1}{2} & 0
 +
\end{bmatrix} \begin{bmatrix}
 +
  1 & 0 & 0  \\
 +
  0 & \frac{1}{2} & -\frac{1}{2} 
 +
\end{bmatrix} =  \begin{bmatrix}
 +
  \frac{1}{2} & 0 & 0  \\
 +
  0 & \frac{1}{2} & -\frac{1}{2}  \\
 +
  -\frac{1}{2} & 0 & 0
 +
\end{bmatrix}</math>
 +
 +
      <math> x^{\ast} = A^{\dagger} b = \begin{bmatrix}
 +
  \frac{1}{2} & 0 & 0  \\
 +
  0 & \frac{1}{2} & -\frac{1}{2}  \\
 +
  -\frac{1}{2} & 0 & 0
 +
\end{bmatrix}\begin{bmatrix}
 +
  0 \\
 +
  \frac{1}{2} \\
 +
  1
 +
\end{bmatrix} = \begin{bmatrix}
 +
  0 \\
 +
  \frac{1}{2} \\
 +
  0
 +
\end{bmatrix}</math>
  
 
<br>  
 
<br>  
Line 410: Line 472:
  
 
       Therefore, <span class="texhtml">''x''<sub>1</sub> = 3,</span> <span class="texhtml">''x''<sub>2</sub> = 6,</span> maximize the function.
 
       Therefore, <span class="texhtml">''x''<sub>1</sub> = 3,</span> <span class="texhtml">''x''<sub>2</sub> = 6,</span> maximize the function.
 +
 +
Solution 2:
 +
 +
The standard form for this problem is
 +
        <span class="texhtml">''Mnimize''</span>'''    <span class="texhtml"> − ''x''<sub>1</sub> − 2''x''<sub>2</sub></span>'''
 +
        <span class="texhtml">''Sbject to''</span>'''    <span class="texhtml"> − 2''x''<sub>1</sub> + ''x''<sub>2</sub> + ''x''<sub>3</sub> = 2</span>'''
 +
                        <span class="texhtml"> − ''x''<sub>1</sub> + ''x''<sub>2</sub> + ''x''<sub>4</sub> = 3</span>
 +
                        <span class="texhtml">''x''<sub>1</sub> + ''x''<sub>5</sub> = 3</span>
 +
                        <math>x_1, x_2, x_3, x_4, x_5 \ge 0.</math>
 +
 +
<math>\begin{matrix}
 +
  a_1 & a_2 & a_3 & a_4 & a_5 & b \\
 +
  -2 & 1& 1 &0& 0& 2 \\
 +
  -1& 1& 0 &1 &0 &3\\
 +
  1 &0& 0& 0& 1& 3\\
 +
  -1& -2 &0& 0& 0& 0
 +
\end{matrix}\Rightarrow
 +
 +
\begin{matrix}
 +
  a_1 & a_2 & a_3 & a_4 & a_5 & b \\
 +
  -2 & 1& 1 &0& 0& 2 \\
 +
  1& 0& -1 &1 &0 &1\\
 +
  1 &0& 0& 0& 1& 3\\
 +
  -5& 0 &2& 0& 0& 4
 +
\end{matrix}
 +
\Rightarrow
 +
\begin{matrix}
 +
  a_1 & a_2 & a_3 & a_4 & a_5 & b \\
 +
  0 & 1& -1 &2& 0& 4 \\
 +
  1& 0& -1 &1 &0 &1\\
 +
  0 &0& 1& -1& 1& 2\\
 +
  0& 0 &-3& 5& 0& 9
 +
\end{matrix}
 +
\Rightarrow
 +
\begin{matrix}
 +
  a_1 & a_2 & a_3 & a_4 & a_5 & b \\
 +
  0 & 1& 0 &1& 1& 6 \\
 +
  1& 0& 0 &0 &1 &3\\
 +
  0 &0& 1& -1& 1& 2\\
 +
  0& 0 &0& 2& 3& 15
 +
\end{matrix}</math>
 +
 +
From the last tableau, we can see that <math>\begin{bmatrix}
 +
  x^{1} = 3\\ x^{2} = 6 
 +
\end{bmatrix}</math>
 +
maximizes the object function. The maximum value is 15
  
 
<br> '''Problem 5.(20pts) Solve the following problem:'''  
 
<br> '''Problem 5.(20pts) Solve the following problem:'''  
Line 517: Line 625:
 
   x^{\ast} = 1\\ x^{\ast} = 0  
 
   x^{\ast} = 1\\ x^{\ast} = 0  
 
  \end{bmatrix}</math> is the minimized point.
 
  \end{bmatrix}</math> is the minimized point.
 +
 +
Solution 2:
 +
 +
Because in both the object and subject function, the term related to <math>x_1</math> is <math>x_1^{2}</math>, which doesn't depend on the sign of <math>x_1</math>, we can ignore the constraint function <math> x_1 >0 </math> for now.
 +
 +
The standard form of the optimizing problem is
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;
 +
<math>\text{minimize } -x_1^2 + 2x_2</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>\text{subject to  }  g_1(x)=x_1^2+x_2^2-1 \le 0  \text { and }  g_2(x)=-x_2 \le 0</math>
 +
 +
The KKT Condition.
 +
      <math>1. \mu _1, \mu _2 \ge 0</math> 　
 +
      <math>2.\begin{bmatrix}
 +
  -2x_1+2\mu_1 x_1 \\ 2+2\mu_1 x_2-\mu_2
 +
\end{bmatrix}=0</math>
 +
      <math>3. (x_1^2+x_2^2-1)\mu_1 - x_2\mu _2 = 0</math> 　
 +
      <math>4. g_1(x),g_2(x) \le 0</math> 　
 +
 +
<math>\text {Case 1: } \mu_1=\mu_2=0 \text{, which implies } 2=0 \Rightarrow impossible </math>
 +
 +
<math>\text {Case 2: } \mu_1 \neq 0,  \mu_2=0 \Rightarrow {\begin{matrix}
 +
\nabla l\left( x,\mu \right)=\begin{pmatrix}
 +
-4+\mu_{2}-\mu_{3}\\
 +
-2-\mu_{1}-\mu_{2}
 +
\end{pmatrix}= \begin{pmatrix} 0 \\ 0\end{pmatrix} \\
 +
-2\mu_{2}=0
 +
\end{matrix}\right. \Rightarrow \left\{\begin{matrix}
 +
\mu_{1}=-2\\
 +
\mu_{2}=0\\
 +
\mu_{3}=-4
 +
\end{matrix}\right.</math>

Revision as of 21:08, 25 January 2013

AC - 3 August 2012 QE

Problem 1. (20 pts)

(i) (10 pts) Find the factor by which the uncertainty range is reduced when using the Fibonacci method. Assume that the last step has the form

$ \begin{align} 1- \rho_{N-1} = \frac{F_{2}}{F_{3}} = \frac{2}{3}, \end{align} $

where N − 1 is the number of steps performed in the uncertainty range reduction process.



Solution: 

   The reduction factor is (1 − ρ1)(1 − ρ2)(1 − ρ3)...(1 − ρN − 1)
Since 
$  1- \rho_{N-1} = \frac{F_{2}}{F_{3}} = \frac{2}{3},  $
we have 
$  1- \rho_{N-2} = \frac{F_{3}}{F_{4}} $     and so on.

   Then, we have
$  (1- \rho_{1})(1- \rho_{2})(1- \rho_{3})...(1- \rho_{N-1}) = \frac{F_{N}}{F_{N+1}}   \frac{F_{N-1}}{F_{N}}   ...   \frac{F_{2}}{F_{3}} = \frac{F_{2}}{F_{N+1}} $
Therefore, the reduction factor is
$ \frac{2}{F_{N+1}} $


Solution 2:

The uncertainty interval is reduced by $ (1- \rho_{1})(1- \rho_{2})(1- \rho_{3})...(1- \rho_{N-1}) = \frac{F_{N}}{F_{N+1}} \frac{F_{N-1}}{F_{N}} ... \frac{F_{2}}{F_{3}} = \frac{F_{2}}{F_{N+1}} $



(ii)(10 pts) It is known that the minimizer of a certain function f(x) is located in the interval[-5, 15]. What is the minimal number of iterations of the Fibonacci method required to box in the minimizer within the range 1.0? Assume that the last useful value of the factor reducing the uncertainty range is 2/3, that is

$ 1- \rho_{N-1} = \frac{F_{2}}{F_{3}} = \frac{2}{3}, $


Solution: 

      Final Range: 1.0; Initial Range: 20.

      $  \frac{2}{F_{N+1}} \le \frac{1.0}{20} $, or $  F_{N+1} \ge 40 $ 

      So, N + 1 = 9

      Therefore, the minimal iterations is N-1 or 7.


Solution 2:

Since the final range is 1.0 and the initial range is 20, we can say Failed to parse (syntax error): \frac{2}{F_{N+1}} \le \frac{1.0}{20} or equivalently F_{N+1}} \ge 40

From the inequality, we get $ F_{N+1} \ge 40 , so N+1=9 $. Therefore the minimum number of iteration is N-1=7


Problem 2. (20pts) Employ the DFP method to construct a set of Q-conjugate directions using the function 

      $ f = \frac{1}{2}x^TQx - x^Tb+c  $
     $   =\frac{1}{2}x^T  \begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix}x-x^T\begin{bmatrix}   2  \\   1  \end{bmatrix} + 3. $

Where x(0) is arbitrary.


Solution: 

      $ f = \frac{1}{2}x^TQx - x^Tb+c  $
   Use initial  point x(0) = [0,0]T and H0 = I2
   I'n <span class="texhtml" />his <span class="texhtml" />case,
   $ g^{(k)} = \begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix} x^{(k)} - \begin{bmatrix}   2  \\   1  \end{bmatrix} $

      H'e'n'c'e, 
   $ g^{(0)} = \begin{bmatrix}   -2  \\   -1  \end{bmatrix}, $  $ d^{(0)} = -H_0g^{(0)} =- \begin{bmatrix}   1 & 0 \\   0 & 1  \end{bmatrix}\begin{bmatrix}   -2  \\   -1  \end{bmatrix} = \begin{bmatrix}   2  \\   1  \end{bmatrix} $



      Because f is aquadraticfunction,

      $ \alpha_0 = argminf(x^{(0)} + \alpha d^{(0)}) = - \frac{g^{(0)^T}d^{(0)}}{d^{(0)^T}Qd^{(0)}} = - \frac{\begin{bmatrix}   -2 & -1   \end{bmatrix}\begin{bmatrix}   2  \\   1  \end{bmatrix}}{\begin{bmatrix}   2 & 1\end{bmatrix}\begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix}\begin{bmatrix}   2  \\   1  \end{bmatrix}} = \frac{1}{2} $

      $ x^{(1)} = x^{(0)} + \alpha d^{(0)} = \frac{1}{2} \begin{bmatrix}   2  \\   1  \end{bmatrix} = \begin{bmatrix}   1  \\   \frac{1}{2}  \end{bmatrix} $

      $ \Delta x^{(0)} = x^{(1)}- x^{(0)} = \begin{bmatrix}   1  \\   \frac{1}{2}  \end{bmatrix} $
   $ g^{(1)} =\begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix} x^{(1)} - \begin{bmatrix}   2  \\   1  \end{bmatrix}= \begin{bmatrix}   -\frac{1}{2}  \\   1  \end{bmatrix} $

      $ \Delta g^{(0)} = g^{(1)} - g^{(0)} = \begin{bmatrix}   -\frac{3}{2}  \\   2  \end{bmatrix}  $



      O'b's'e'r'v'e t'h'a't:
   $ \Delta x^{(0)} \Delta x^{(0)^T} = \begin{bmatrix}   1  \\   \frac{1}{2}   \end{bmatrix} \begin{bmatrix}   1  & \frac{1}{2}   \end{bmatrix} = \begin{bmatrix}   1 & \frac{1}{2}  \\   \frac{1}{2}  & \frac{1}{4}   \end{bmatrix}  $
   $  \Delta x^{(0)^T} \Delta g^{(0)} = \begin{bmatrix}   1  & \frac{1}{2}   \end{bmatrix}\begin{bmatrix}   \frac{3}{2}   \\   2   \end{bmatrix}  = \frac{5}{2} $
   $ H_0 \Delta g^{(0)} = \begin{bmatrix}   1 & 0 \\   0 & 1  \end{bmatrix} \begin{bmatrix}   \frac{3}{2}   \\   2   \end{bmatrix} = \begin{bmatrix}   \frac{3}{2}   \\   2   \end{bmatrix}, $ $ (H_0 \Delta g^{(0)})(H_0 \Delta g^{(0)})^T = \begin{bmatrix}   \frac{9}{4}  & 3 \\   3 & 4  \end{bmatrix} $
   $ \Delta g^{(0)^T}H_0 \Delta g^{(0)} = \begin{bmatrix}   \frac{3}{2}  & 2   \end{bmatrix} \begin{bmatrix}   1 & 0 \\   0 & 1  \end{bmatrix} \begin{bmatrix}   \frac{3}{2}  \\ 2   \end{bmatrix} = \frac{25}{4} $
   Using the above, now we have
   $ H_1 = H_0 + \frac{\Delta x^{(0)} \Delta x^{(0)^T}}{\Delta x^{(0)^T} \Delta g^{(0)}} - \frac{(H_0 \Delta g^{(0)})(H_0 \Delta g^{(0)})^T}{\Delta g^{(0)^T}H_0 \Delta g^{(0)} }  = \begin{bmatrix}   1 & 0 \\   0 & 1  \end{bmatrix} + \begin{bmatrix}   \frac{2}{5} & \frac{1}{5} \\   \frac{1}{5} & \frac{1}{10}  \end{bmatrix} - \frac{25}{4}\begin{bmatrix}   \frac{9}{4} & 3 \\   3 & 4  \end{bmatrix} = \begin{bmatrix}   \frac{26}{25} & -\frac{7}{25} \\   -\frac{7}{25} & \frac{23}{50}  \end{bmatrix} $

      T'hen we have,
   $ d^{(1)} = -H_1 g^{(0)} = - \begin{bmatrix}   \frac{26}{25} & -\frac{7}{25} \\   -\frac{7}{25} & \frac{23}{50}  \end{bmatrix} \begin{bmatrix}   -\frac{1}{2}  \\   1  \end{bmatrix} = \begin{bmatrix}   \frac{4}{5}  \\   -\frac{3}{5}  \end{bmatrix} $

      $ \alpha_1 = argminf(x^{(1)} + \alpha d^{(1)}) = - \frac{g^{(1)^T}d^{(1)}}{d^{(1)^T}Qd^{(1)}} = - \frac{\begin{bmatrix}   -2 & 1   \end{bmatrix}\begin{bmatrix}   \frac{4}{5}  \\   -\frac{3}{5}  \end{bmatrix}}{\begin{bmatrix}   \frac{4}{5} & -\frac{3}{5}\end{bmatrix}\begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix}\begin{bmatrix}   \frac{4}{5}  \\   -\frac{3}{5}  \end{bmatrix}} = \frac{5}{2} $

      $ x^{(2)} = x^{(1)} + \alpha_1 d^{(1)} = \begin{bmatrix}   1  \\   \frac{1}{2}  \end{bmatrix} + \frac{5}{2}\begin{bmatrix}   \frac{4}{5}  \\   -\frac{3}{5}  \end{bmatrix} = \begin{bmatrix}   3  \\   -1  \end{bmatrix}  $

      $ \Delta x^{(1)} = x^{(2)} - x^{(1)} = \begin{bmatrix}   2  \\   -\frac{3}{2}  \end{bmatrix} $
   $ g^{(2)} = \begin{bmatrix}   1 & 1 \\   1 & 2  \end{bmatrix} x^{(0)} - \begin{bmatrix}   2  \\   1  \end{bmatrix} = \begin{bmatrix}   0  \\   0  \end{bmatrix} $

      Note that we have $ d^{(0)^T}Qd^{(0)} = 0; $
   that is, $ d^{(0)} = \begin{bmatrix}   2  \\   1  \end{bmatrix} $ a'n'd $ d^{(1)}  = \begin{bmatrix}   \frac{4}{5}  \\   -\frac{3}{5}  \end{bmatrix} $ are Q-conjugate directions.


Problem 3. (20pts) For the system of linear equations,<span class="texhtml" />'x = b, where

$ A = \begin{bmatrix} 1 & 0 &-1 \\ 0 & 1 & 0 \\ 0 & -1& 0 \end{bmatrix}, b = \begin{bmatrix} 0 \\ 2 \\ 1 \end{bmatrix} $


Find the minimum length vector $ x^{(\ast)} $ that minimizes $ \| Ax -b\|^{2}_2 $



Solutions: 

      $ A = BC = \begin{bmatrix}   1 & 0  \\   0 & 1   \\   0 & -1  \end{bmatrix} \begin{bmatrix}   1 & 0 &-1  \\   0 & 1 & 0    \end{bmatrix} $

      $ B^{\dagger} = (B^T B)^{-1}B^T = \begin{bmatrix}   1 & 0 \\   0 & 2    \end{bmatrix}^{-1} \begin{bmatrix}   1 & 0 & 0  \\   0 & 1 & -1    \end{bmatrix} = \begin{bmatrix}   1 & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}    \end{bmatrix} $

      $ C^{\dagger} = C^T(CC^T)^{-1} =\begin{bmatrix}   1 & 0  \\   0 & 1   \\   -1 & 0  \end{bmatrix} \begin{bmatrix}   2 & 0 \\   0 & 1    \end{bmatrix}^{-1} = \begin{bmatrix}   \frac{1}{2} & 0  \\   0 & 1   \\   -\frac{1}{2} & 0  \end{bmatrix}  $

      $ A^{\dagger} = C^{\dagger}B^{\dagger} =\begin{bmatrix}   \frac{1}{2} & 0  \\   0 & 1   \\   -\frac{1}{2} & 0  \end{bmatrix} \begin{bmatrix}   1 & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}    \end{bmatrix} =  \begin{bmatrix}   \frac{1}{2} & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}  \\   -\frac{1}{2} & 0 & 0  \end{bmatrix} $

      $  x^{\ast} = A^{\dagger} b = \begin{bmatrix}   \frac{1}{2} & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}  \\   -\frac{1}{2} & 0 & 0  \end{bmatrix}\begin{bmatrix}   0 \\   \frac{1}{2} \\   1  \end{bmatrix} = \begin{bmatrix}   0 \\   \frac{1}{2} \\   0  \end{bmatrix} $


Solution 2:

$ x^{(\ast)}=A^{\dagger}b $

Since $ A = BC = \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 0 & -1 \end{bmatrix} \begin{bmatrix} 1 & 0 &-1 \\ 0 & 1 & 0 \end{bmatrix} $

$ B^{\dagger} = (B^T B)^{-1}B^T = \begin{bmatrix} 1 & 0 \\ 0 & 2 \end{bmatrix}^{-1} \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & -1 \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \frac{1}{2} & -\frac{1}{2} \end{bmatrix} $ 

      $ C^{\dagger} = C^T(CC^T)^{-1} =\begin{bmatrix}   1 & 0  \\   0 & 1   \\   -1 & 0  \end{bmatrix} \begin{bmatrix}   2 & 0 \\   0 & 1    \end{bmatrix}^{-1} = \begin{bmatrix}   \frac{1}{2} & 0  \\   0 & 1   \\   -\frac{1}{2} & 0  \end{bmatrix}  $

      $ A^{\dagger} = C^{\dagger}B^{\dagger} =\begin{bmatrix}   \frac{1}{2} & 0  \\   0 & 1   \\   -\frac{1}{2} & 0  \end{bmatrix} \begin{bmatrix}   1 & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}    \end{bmatrix} =  \begin{bmatrix}   \frac{1}{2} & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}  \\   -\frac{1}{2} & 0 & 0  \end{bmatrix} $

      $  x^{\ast} = A^{\dagger} b = \begin{bmatrix}   \frac{1}{2} & 0 & 0  \\   0 & \frac{1}{2} & -\frac{1}{2}  \\   -\frac{1}{2} & 0 & 0  \end{bmatrix}\begin{bmatrix}   0 \\   \frac{1}{2} \\   1  \end{bmatrix} = \begin{bmatrix}   0 \\   \frac{1}{2} \\   0  \end{bmatrix} $


Problem 4. (20pts) Use any simplex method to solve the following linear program. 

           Maximize    x1 + 2x2
        S'ubject to    $ -2x_1+x_2 \le 2 $
                       $ x_1-x_2 \ge -3 $
                       $ x_1 \le -3 $
                       $ x_1 \ge 0, x_2 \ge 0. $



Solution: 

        We can transfer the problem to the following standard form:
        Mnimize     − x1 − 2x2
        Sbject to     − 2x1 + x2 + x3 = 2
                        − x1 + x2 + x4 = 3
                       x1 + x5 = 3
                       $ x_1, x_2, x_3, x_4, x_5 \ge 0. $

       We form the corresponding tableau for the problem
     $ \begin{matrix}   a_1 & a_2 & a_3 & a_4 & a_5 & b \\   -2 & 1& 1 &0& 0& 2 \\   -1& 1& 0 &1 &0 &3\\   1 &0& 0& 0& 1& 3\\   -1& -2 &0& 0& 0& 0  \end{matrix} $
      
Since it is already in canonical form. Because r2 =  − 7, we bring a2 into the basis.

      After computing the ratios, we pivot about the (1,2) element of the tableau to obtain
     $ \begin{matrix}   a_1 & a_2 & a_3 & a_4 & a_5 & b \\   -2 & 1& 1 &0& 0& 2 \\   1& 0& -1 &1 &0 &1\\   1 &0& 0& 0& 1& 3\\   -5& 0 &2& 0& 0& 4  \end{matrix} $

      Similarly, we pivot about the (2,1) element to obtain 
     $ \begin{matrix}   a_1 & a_2 & a_3 & a_4 & a_5 & b \\   0 & 1& -1 &2& 0& 4 \\   1& 0& -1 &1 &0 &1\\   0 &0& 1& -1& 1& 2\\   0& 0 &-3& 5& 0& 9  \end{matrix} $

      Similarly, we pivot about the (3,3) element to obtain 
     $ \begin{matrix}   a_1 & a_2 & a_3 & a_4 & a_5 & b \\   0 & 1& 0 &1& 1& 6 \\   1& 0& 0 &0 &1 &3\\   0 &0& 1& -1& 1& 2\\   0& 0 &0& 2& 3& 15  \end{matrix} $

      Therefore, x1 = 3, x2 = 6, maximize the function.

Solution 2:

The standard form for this problem is 

        Mnimize     − x1 − 2x2
        Sbject to     − 2x1 + x2 + x3 = 2
                        − x1 + x2 + x4 = 3
                       x1 + x5 = 3
                       $ x_1, x_2, x_3, x_4, x_5 \ge 0. $

$ \begin{matrix} a_1 & a_2 & a_3 & a_4 & a_5 & b \\ -2 & 1& 1 &0& 0& 2 \\ -1& 1& 0 &1 &0 &3\\ 1 &0& 0& 0& 1& 3\\ -1& -2 &0& 0& 0& 0 \end{matrix}\Rightarrow \begin{matrix} a_1 & a_2 & a_3 & a_4 & a_5 & b \\ -2 & 1& 1 &0& 0& 2 \\ 1& 0& -1 &1 &0 &1\\ 1 &0& 0& 0& 1& 3\\ -5& 0 &2& 0& 0& 4 \end{matrix} \Rightarrow \begin{matrix} a_1 & a_2 & a_3 & a_4 & a_5 & b \\ 0 & 1& -1 &2& 0& 4 \\ 1& 0& -1 &1 &0 &1\\ 0 &0& 1& -1& 1& 2\\ 0& 0 &-3& 5& 0& 9 \end{matrix} \Rightarrow \begin{matrix} a_1 & a_2 & a_3 & a_4 & a_5 & b \\ 0 & 1& 0 &1& 1& 6 \\ 1& 0& 0 &0 &1 &3\\ 0 &0& 1& -1& 1& 2\\ 0& 0 &0& 2& 3& 15 \end{matrix} $

From the last tableau, we can see that $ \begin{bmatrix} x^{1} = 3\\ x^{2} = 6 \end{bmatrix} $ maximizes the object function. The maximum value is 15


Problem 5.(20pts) Solve the following problem: 

           Minimize    $ -x_1^2 + 2x_2 $
        Subject to    $ x_1^2+x_2^2 \le 1 $
                       $  x_1 \ge 0 $
                       $ x_2 \ge 0 $

(i)(10pts) Find the points that satisfy the KKT condition.

Solution: 

        Standard Form:
        Minimize    $ -x_1^2 + 2x_2 $
        Su   $ g_1(x) = x_1^2+x_2^2 - 1 \le 0 $
                       $ g_2(x) = -x_2 \le 0 $

        The KKT Condition.
     $ 1. \mu _1, \mu _2 \ge 0 $ 　
     2. − 2x1 + 2x1μ1 = 0 　
       2.2 + 2x2μ1 − μ2 = 0 　
     $ 3. (x_1^2+x_2^2-1)\mu_1 - x_2\mu _2 = 0 $ 　
     $ 4. g_1(x),g_2(x) \le 0 $ 　



        Case 1:
     I'f μ1 = 0 and μ2 = 0,
           t'h'e'n,2 = 0which is impossible.
     
     Case 2:
     I'f μ1 = 0 and $ \mu_2 \not= 0, $
           t'h'e'n,μ2 = 2,x1 = 0,x2 = 0..

        Case 3:
     I'f $ \mu_1 \not= 0 $ and μ2 = 0,
           $ then, x_1^2 + x_2^2 = 1  $
                     $ if x1 \not= 0, then \mu_1 = 1, x_2 = -1  $which is impossible.
                     if x1 = 0,then x2 = 1,μ1 =  − 1which is impossible.

        Case 4:
     I'f $ \mu_1 \not= 0 $ and $ \mu_2 \not= 0, $
           $ then, x_2 = 0, x_1^2 + x_2^2 = 1. So,  x_1 = 1, \mu_1 = 1, \mu_2 = 2.  $.

        Therefore, there are two points that satisfy KKT condition.

     $ \begin{bmatrix}   \mu_1 ^{\ast}= 0 \\ \mu_2 ^{\ast}= 2 \\ x_1^{\ast}= 0 \\x_2^{\ast}= 0   \end{bmatrix}and \begin{bmatrix}   \mu_1 ^{\ast}= 1 \\ \mu_2 ^{\ast}= 2 \\ x_1^{\ast}= 1 \\x_2^{\ast}= 0   \end{bmatrix}  $


(ii)(10pts)Apply the SONC and SOSC to determine the nature of the critical points from the previous part.

Solution: 

        Apply SOSC to the first point.
     $ \nabla g_2(x)^t y = 0 $
     $ \begin{bmatrix}   0 & -1  \end{bmatrix} y = 0 $
     $ y = \begin{bmatrix}   a \\ 0  \end{bmatrix},  a\not= 0 $

        $ L(x,\mu) = F(x) + \mu_2 G_2(x) =\begin{bmatrix}   -2 & 0 \\ 0 & 0   \end{bmatrix}  $
     Check ytL(x,μ)y
     $ \begin{bmatrix}   a & 0  \end{bmatrix}\begin{bmatrix}   -2 & 0 \\ 0 & 0   \end{bmatrix} \begin{bmatrix}   a \\ 0  \end{bmatrix} = -2a^2 \lneq 0 $ which means SOSC fails.



        Apply SOSC to the second point.
     $ \nabla g_1(x)^t y = 0,  $

        $ \begin{bmatrix}   2x_1 & 2x_2  \end{bmatrix} y = 0 $
     $ y = \begin{bmatrix}   0 \\ a  \end{bmatrix} where a\not= 0 $

        $ L(x,\mu) = F(x) + \mu_1 G_1(x) + \mu_2 G_2(x) =\begin{bmatrix}   -2 & 0 \\ 0 & 0   \end{bmatrix}  + \begin{bmatrix}   2 & 0 \\ 0 & 2    \end{bmatrix}  = \begin{bmatrix}   0 & 0 \\ 0 & 2   \end{bmatrix}  $
     Check ytL(x,μ)y
     $ \begin{bmatrix}   0 & a  \end{bmatrix}\begin{bmatrix}   0 & 0 \\ 0 & 2   \end{bmatrix} \begin{bmatrix}   0 \\ a  \end{bmatrix} = 2a^2 \gneq 0 $ that implies
     $ \begin{bmatrix}   x^{\ast} = 1\\ x^{\ast} = 0   \end{bmatrix} $ is the minimized point.

Solution 2:

Because in both the object and subject function, the term related to $ x_1 $ is $ x_1^{2} $, which doesn't depend on the sign of $ x_1 $, we can ignore the constraint function $ x_1 >0 $ for now.

The standard form of the optimizing problem is                                                                                                                                                                                          $ \text{minimize } -x_1^2 + 2x_2 $

                        $ \text{subject to } g_1(x)=x_1^2+x_2^2-1 \le 0 \text { and } g_2(x)=-x_2 \le 0 $

The KKT Condition. 

     $ 1. \mu _1, \mu _2 \ge 0 $ 　
     $ 2.\begin{bmatrix}   -2x_1+2\mu_1 x_1 \\ 2+2\mu_1 x_2-\mu_2   \end{bmatrix}=0 $
     $ 3. (x_1^2+x_2^2-1)\mu_1 - x_2\mu _2 = 0 $ 　
     $ 4. g_1(x),g_2(x) \le 0 $ 　

$ \text {Case 1: } \mu_1=\mu_2=0 \text{, which implies } 2=0 \Rightarrow impossible $

$ \text {Case 2: } \mu_1 \neq 0, \mu_2=0 \Rightarrow {\begin{matrix} \nabla l\left( x,\mu \right)=\begin{pmatrix} -4+\mu_{2}-\mu_{3}\\ -2-\mu_{1}-\mu_{2} \end{pmatrix}= \begin{pmatrix} 0 \\ 0\end{pmatrix} \\ -2\mu_{2}=0 \end{matrix}\right. \Rightarrow \left\{\begin{matrix} \mu_{1}=-2\\ \mu_{2}=0\\ \mu_{3}=-4 \end{matrix}\right. $

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Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett