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---- | ---- | ||
− | + | '''Theorem: ''' | |
− | + | A basic feasible solution is optimal if and only if the corresponding reduced cost coefficeints are all nonnegative. | |
+ | '''Simplex Method:''' | ||
+ | 1. Transform the given problem into standard form by introducing slack variables <span class="texhtml">''x''<sub>3</sub></span> and <span class="texhtml">''x''<sub>4</sub></span>.<span class="texhtml"><sub></sub></span> | ||
+ | 2. Form a canonical augmented matrix corresponding to an initial basic feasible solution. | ||
+ | 3. Calculate the reduced cost coefficients corresponding to the nonbasic variables. | ||
+ | |||
+ | 4. If <math>r_{j}>\geq0</math> for all <math>j</math>, stop. -- the current basic feasible solution is optimal. | ||
+ | |||
+ | 5. Select a <math>q</math> such that <math>r_{q}<0</math> | ||
+ | |||
+ | 6. If no <math>y_{iq}>0</math>, stop. -- the problem is unbounded; else, calculate <math>p=argmin_{i}\left \{ y_{i0}/y_{iq}:y_{iq}>0 \right \}</math>. | ||
+ | |||
+ | 7. Update the canonical augmented matrix by pivoting about the <math>(p, q)</math> th element. | ||
+ | |||
+ | 8. Go to step 3.<br> | ||
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<math>\therefore \text{the optimal solution to the original problem is } x^{*}= \begin{bmatrix} 4\\ 2 \end{bmatrix}</math><font face="serif" color="#ff0000" style="font-size: 17px;">'''<br>'''</font> | <math>\therefore \text{the optimal solution to the original problem is } x^{*}= \begin{bmatrix} 4\\ 2 \end{bmatrix}</math><font face="serif" color="#ff0000" style="font-size: 17px;">'''<br>'''</font> | ||
− | The maximum value for <font face="serif"><span class="texhtml"> ''x''<sub>1</sub> + ''x''<sub>2</sub> is 6</span><br></font> | + | The maximum value for <font face="serif"><span class="texhtml"> ''x''<sub>1</sub> + ''x''<sub>2</sub> is 6.</span><br></font> |
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Go to | Go to | ||
− | *Part 1: [[ECE-QE AC3-2011 solusion-1|solutions and discussions]] | + | *Part 1: [[ECE-QE AC3-2011 solusion-1|solutions and discussions]]<br> |
− | + | ||
*Part 3: [[ECE-QE AC3-2011 solusion-3|solutions and discussions]] | *Part 3: [[ECE-QE AC3-2011 solusion-3|solutions and discussions]] | ||
*Part 4: [[ECE-QE AC3-2011 solusion-4|solutions and discussions]] | *Part 4: [[ECE-QE AC3-2011 solusion-4|solutions and discussions]] |
Revision as of 18:34, 28 June 2012
Contents
ECE Ph.D. Qualifying Exam in "Automatic Control" (AC)
Question 3, Part 2, August 2011
$ \color{blue}\text{2. } \left( \text{20 pts} \right) \text{ Use the simplex method to solve the problem, } $
maximize x1 + x2
$ \text{subject to } x_{1}-x_{2}\leq2 $
$ x_{1}+x_{2}\leq6 $
$ x_{1},-x_{2}\geq0. $
Theorem:
A basic feasible solution is optimal if and only if the corresponding reduced cost coefficeints are all nonnegative.
Simplex Method:
1. Transform the given problem into standard form by introducing slack variables x3 and x4.
2. Form a canonical augmented matrix corresponding to an initial basic feasible solution.
3. Calculate the reduced cost coefficients corresponding to the nonbasic variables.
4. If $ r_{j}>\geq0 $ for all $ j $, stop. -- the current basic feasible solution is optimal.
5. Select a $ q $ such that $ r_{q}<0 $
6. If no $ y_{iq}>0 $, stop. -- the problem is unbounded; else, calculate $ p=argmin_{i}\left \{ y_{i0}/y_{iq}:y_{iq}>0 \right \} $.
7. Update the canonical augmented matrix by pivoting about the $ (p, q) $ th element.
8. Go to step 3.
$ \color{blue}\text{Solution 1:} $
min − x1 − x2
subject to x1 − x2 + x3 = 2
x1 + x2 + x4 = 6
$ x_{1},x_{2},x_{3},x_{4}\geq 0 $
$ \begin{matrix} 1 & -1 & 1 & 0 & 2\\ 1 & 1 & 0 & 1 & 6 \\ -1 & -1 & 0 & 0 & 0 \end{matrix} \Rightarrow \begin{matrix} 1 & -1 & 1 & 0 & 2\\ 0 & 2 & -1 & 1 & 4 \\ 0 & -2 & 1 & 0 & 2 \end{matrix} \Rightarrow \begin{matrix} 1 & 0 & \frac{1}{2} & \frac{1}{2} & 4\\ 0 & 1 & -\frac{1}{2} & \frac{1}{2} & 2 \\ 0 & 0 & 0 & 1 & 6 \end{matrix} $
$ \Rightarrow x_{1}=4, x_{2}=2, \text{the maximum value } x_{1}+x_{2}=6 $
$ \color{blue}\text{Solution 2:} $
Get standard form for simplex method min − x1 − x2
subject to x1 − x2 + x3 = 2
x1 + x2 + x4 = 6
$ x_{i}\geq0, i=1,2,3,4 $
$ \begin{matrix} & a_{1} & a_{2} & a_{3} & a_{4} & b\\ & 1 & -1 & 1 & 0 & 2\\ & 1 & 1 & 0 & 1 & 6 \\ c^{T} & -1 & -1 & 0 & 0 & 0 \end{matrix} $ $ \Rightarrow \begin{matrix} 1 & -1 & 1 & 0 & 2\\ 1 & 1 & 0 & 1 & 6 \\ 0 & 0 & 0 & 1 & 6 \end{matrix} \Rightarrow \begin{matrix} 1 & -1 & 1 & 0 & 2\\ 0 & 2 & -1 & 1 & 4 \\ 0 & 0 & 0 & 1 & 6 \end{matrix} \Rightarrow \begin{matrix} 1 & 0 & \frac{1}{2} & \frac{1}{2} & 4\\ 0 & 1 & -\frac{1}{2} & \frac{1}{2} & 2 \\ 0 & 0 & 0 & 1 & 6 \end{matrix} $
$ \therefore \text{the optimal solution to the original problem is } x^{*}= \begin{bmatrix} 4\\ 2 \end{bmatrix} $
The maximum value for x1 + x2 is 6.
Automatic Control (AC)- Question 3, August 2011
Go to
- Part 1: solutions and discussions
- Part 3: solutions and discussions
- Part 4: solutions and discussions
- Part 5: solutions and discussions