Line 19: Line 19:
 
<math>\Rightarrow  x_{1}=5-2x_{2}+x_{3}=3-\frac{3}{2}x_{2}+\frac{1}{2}x_{3}</math>  
 
<math>\Rightarrow  x_{1}=5-2x_{2}+x_{3}=3-\frac{3}{2}x_{2}+\frac{1}{2}x_{3}</math>  
  
<math>\Rightarrow x_{2}-x_{3}=4</math>  
+
<math>\Rightarrow x_{2}-x_{3}=4</math>
  
It is equivalent to &nbsp;&nbsp;<math>\text{min }  x_{1}+3x_{2}-4x_{3}
+
<math>\text{It is equivalent to  min }  x_{1}+3x_{2}-4x_{3}
 
=5-2x_{2}+x_{3}+3x_{2}-4x_{3} = x_{2}-3x_{3}+5, x_{2}\geq0, x_{3}\leq0</math>  
 
=5-2x_{2}+x_{3}+3x_{2}-4x_{3} = x_{2}-3x_{3}+5, x_{2}\geq0, x_{3}\leq0</math>  
  
 
<math>x_{2}-3x_{3}+5 = x_{2}-x_{3}-2x_{3}+5=9-2x_{3}\geq9</math>  
 
<math>x_{2}-3x_{3}+5 = x_{2}-x_{3}-2x_{3}+5=9-2x_{3}\geq9</math>  
  
Equicalently,&nbsp;<math>-x_{1}-3x_{2}+4x_{3}\leq-9</math>  
+
<math>\text{Equivalently, } -x_{1}-3x_{2}+4x_{3}\leq-9</math>  
  
Equality is satisfied when&nbsp;<math>x_{3}=0, x_{2} =4, x_{1}=5-2\times4=-3</math>  
+
<math>\text{Equality is satisfied when } x_{3}=0, x_{2} =4, x_{1}=5-2\times4=-3</math>  
  
 
<math>\Rightarrow \left\{\begin{matrix}
 
<math>\Rightarrow \left\{\begin{matrix}
Line 38: Line 38:
 
----
 
----
  
<math>\color{blue}\text{Solution 2:}</math><br> One of the basic feasible solution is an optimal solution.  
+
<math>\color{blue}\text{Solution 2:}</math><br><math>\text{One of the basic feasible solution is an optimal solution.}</math><br>
  
The equality constraints can be represented in the form&nbsp;<math>Ax=b, A= \begin{bmatrix}
+
<math>\text{The equality constraints can be represented in the form } Ax=b, A= \begin{bmatrix}
 
a_{1}&  
 
a_{1}&  
 
a_{2}&  
 
a_{2}&  
 
a_{3}
 
a_{3}
\end{bmatrix}</math>
+
\end{bmatrix}</math>  
  
The fist basis candidate is&nbsp;<math>\begin{pmatrix}
+
<math>\text{The fist basis candidate is } \begin{pmatrix}
 
a_{1} &  
 
a_{1} &  
 
a_{2}  
 
a_{2}  
\end{pmatrix}</math>
+
\end{pmatrix}</math>  
  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
Line 57: Line 57:
 
1 & 0 & 1 & -3 \\  
 
1 & 0 & 1 & -3 \\  
 
0 & 1 & -1 & 4  
 
0 & 1 & -1 & 4  
\end{bmatrix}</math>
+
\end{bmatrix}</math>  
  
 
<math>x^{\left( 1 \right)}= \begin{bmatrix}
 
<math>x^{\left( 1 \right)}= \begin{bmatrix}
 
-3 & 4 & 0
 
-3 & 4 & 0
\end{bmatrix} \text{ is BFS. } f_{1}=-9</math>
+
\end{bmatrix} \text{ is BFS. } f_{1}=-9</math>  
  
The second basis candidate is&nbsp;<math>\begin{pmatrix}
+
<math>\text{The second basis candidate is } \begin{pmatrix}
 
a_{2} &  
 
a_{2} &  
 
a_{3}  
 
a_{3}  
\end{pmatrix}</math>
+
\end{pmatrix}</math>  
  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
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1 & 0 & 1 & -3 \\  
 
1 & 0 & 1 & -3 \\  
 
1 & 1 & 0 & 1  
 
1 & 1 & 0 & 1  
\end{bmatrix}</math>
+
\end{bmatrix}</math>  
  
 
<math>x^{\left( 2 \right)}= \begin{bmatrix}
 
<math>x^{\left( 2 \right)}= \begin{bmatrix}
 
0 & 1 & -3
 
0 & 1 & -3
\end{bmatrix} \text{ is BFS. } f_{2}=-15</math>
+
\end{bmatrix} \text{ is BFS. } f_{2}=-15</math>  
  
The third basis candidate is&nbsp;<math>\begin{pmatrix}
+
<math>\text{The third basis candidate is } \begin{pmatrix}
 
a_{1} &  
 
a_{1} &  
 
a_{3}  
 
a_{3}  
\end{pmatrix}</math>
+
\end{pmatrix}</math>  
  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
 
<math>\left [ A|b \right ] =\begin{bmatrix}  
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0 & 1 & 1 & 4 \\  
 
0 & 1 & 1 & 4 \\  
 
1 & 1 & 0 & 1  
 
1 & 1 & 0 & 1  
\end{bmatrix}</math>
+
\end{bmatrix}</math>  
  
 
<math>x^{\left( 2 \right)}= \begin{bmatrix}
 
<math>x^{\left( 2 \right)}= \begin{bmatrix}
 
1 & 0 & -5
 
1 & 0 & -5
\end{bmatrix} \text{ is BFS. } f_{3}=-21</math>
+
\end{bmatrix} \text{ is BFS. } f_{3}=-21</math>  
  
<math>\because f_{1}>f_{2}>f_{3}</math>
+
<math>\because f_{1}>f_{2}>f_{3}</math>  
  
 
<math>\therefore \text{The optimal solution is } x^{*}=\begin{bmatrix}
 
<math>\therefore \text{The optimal solution is } x^{*}=\begin{bmatrix}
 
-3 & 4 & 0  
 
-3 & 4 & 0  
\end{bmatrix}  \text{ with objective value } -9</math>
+
\end{bmatrix}  \text{ with objective value } -9</math>  
 
+
  
 +
<br>
  
 
----
 
----

Revision as of 14:45, 27 June 2012


ECE Ph.D. Qualifying Exam: Automatic Control (AC)- Question 3, August 2011


 $ \color{blue}\text{3. } \left( \text{20 pts} \right) \text{ Solve the following linear program, } $

maximize    − x1 − 3x2 + 4x3

subject to   x1 + 2x2x3 = 5

                   2x1 + 3x2x3 = 6

                   $ x_{1} \text{ free, } x_{2}\geq0, x_{3}\leq0. $

$ \color{blue}\text{Solution 1:} $

$ \Rightarrow x_{1}=5-2x_{2}+x_{3}=3-\frac{3}{2}x_{2}+\frac{1}{2}x_{3} $

$ \Rightarrow x_{2}-x_{3}=4 $

$ \text{It is equivalent to min } x_{1}+3x_{2}-4x_{3} =5-2x_{2}+x_{3}+3x_{2}-4x_{3} = x_{2}-3x_{3}+5, x_{2}\geq0, x_{3}\leq0 $

$ x_{2}-3x_{3}+5 = x_{2}-x_{3}-2x_{3}+5=9-2x_{3}\geq9 $

$ \text{Equivalently, } -x_{1}-3x_{2}+4x_{3}\leq-9 $

$ \text{Equality is satisfied when } x_{3}=0, x_{2} =4, x_{1}=5-2\times4=-3 $

$ \Rightarrow \left\{\begin{matrix} x_{1}=-3\\ x_{2}=4\\ x_{3}=0 \end{matrix}\right. $


$ \color{blue}\text{Solution 2:} $
$ \text{One of the basic feasible solution is an optimal solution.} $

$ \text{The equality constraints can be represented in the form } Ax=b, A= \begin{bmatrix} a_{1}& a_{2}& a_{3} \end{bmatrix} $

$ \text{The fist basis candidate is } \begin{pmatrix} a_{1} & a_{2} \end{pmatrix} $

$ \left [ A|b \right ] =\begin{bmatrix} 1 & 2 & -1 & 5 \\ 2 & 3 & -1 & 6 \end{bmatrix} = \cdots = \begin{bmatrix} 1 & 0 & 1 & -3 \\ 0 & 1 & -1 & 4 \end{bmatrix} $

$ x^{\left( 1 \right)}= \begin{bmatrix} -3 & 4 & 0 \end{bmatrix} \text{ is BFS. } f_{1}=-9 $

$ \text{The second basis candidate is } \begin{pmatrix} a_{2} & a_{3} \end{pmatrix} $

$ \left [ A|b \right ] =\begin{bmatrix} 1 & 2 & -1 & 5 \\ 2 & 3 & -1 & 6 \end{bmatrix} = \cdots = \begin{bmatrix} 1 & 0 & 1 & -3 \\ 1 & 1 & 0 & 1 \end{bmatrix} $

$ x^{\left( 2 \right)}= \begin{bmatrix} 0 & 1 & -3 \end{bmatrix} \text{ is BFS. } f_{2}=-15 $

$ \text{The third basis candidate is } \begin{pmatrix} a_{1} & a_{3} \end{pmatrix} $

$ \left [ A|b \right ] =\begin{bmatrix} 1 & 2 & -1 & 5 \\ 2 & 3 & -1 & 6 \end{bmatrix} = \cdots = \begin{bmatrix} 0 & 1 & 1 & 4 \\ 1 & 1 & 0 & 1 \end{bmatrix} $

$ x^{\left( 2 \right)}= \begin{bmatrix} 1 & 0 & -5 \end{bmatrix} \text{ is BFS. } f_{3}=-21 $

$ \because f_{1}>f_{2}>f_{3} $

$ \therefore \text{The optimal solution is } x^{*}=\begin{bmatrix} -3 & 4 & 0 \end{bmatrix} \text{ with objective value } -9 $



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