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[[Category:ECE]]
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[[Category:QE]]
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[[Category:CNSIP]]
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[[Category:problem solving]]
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[[Category:automatic control]]
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[[Category:optimization]]
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= [[ECE PhD Qualifying Exams|ECE Ph.D. Qualifying Exam]] in "Automatic Control" (AC)  =
 +
 
 +
= [[ECE-QE_AC3-2011|Question 3, August 2011]], Part 4 =
  
= [[ECE PhD Qualifying Exams|ECE Ph.D. Qualifying Exam]]: Automatic Control (AC)- Question 3, August 2011 =
+
:[[ECE-QE AC3-2011 solusion-1|Part 1]],[[ECE-QE AC3-2011 solusion-2|2]],[[ECE-QE AC3-2011 solusion-3|3]],[[ECE-QE_AC3-2011_solusion-4|4]],[[ECE-QE AC3-2011 solusion-5|5]]
  
 
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&nbsp;<font color="#ff0000"><span style="font-size: 19px;"><math>\color{blue}\text{4. } \left( \text{20 pts} \right) \text{ Consider the following model of a discrete-time system,  }</math></span></font>  
 
&nbsp;<font color="#ff0000"><span style="font-size: 19px;"><math>\color{blue}\text{4. } \left( \text{20 pts} \right) \text{ Consider the following model of a discrete-time system,  }</math></span></font>  
  
<math>x\left ( k+1 \right )=2x\left ( k \right )+u\left ( k \right ), x\left ( 0 \right )=0, 0\leq k\leq 2</math><br>  
+
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>x\left ( k+1 \right )=2x\left ( k \right )+u\left ( k \right ), x\left ( 0 \right )=0, 0\leq k\leq 2</math><br>  
  
 
<math>\color{blue}\text{Use the Lagrange multiplier approach to calculate the optimal control sequence}</math><br>  
 
<math>\color{blue}\text{Use the Lagrange multiplier approach to calculate the optimal control sequence}</math><br>  
  
<math>\left \{ u\left ( 0 \right ),u\left ( 1 \right ), u\left ( 2 \right ) \right \}</math>  
+
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<math>\left \{ u\left ( 0 \right ),u\left ( 1 \right ), u\left ( 2 \right ) \right \}</math>  
  
<math>\color{blue}\text{that transfers the initial state } x\left( 0 \right) \text{ to } x\left( 3 \right)=7 \text{ while minimizing the performance index}</math><br> <math>J=\frac{1}{2}\sum\limits_{k=0}^2 u\left ( k \right )^{2}</math><br>  
+
<math>\color{blue}\text{that transfers the initial state } x\left( 0 \right) \text{ to } x\left( 3 \right)=7 \text{ while minimizing the performance index}</math><br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<math>J=\frac{1}{2}\sum\limits_{k=0}^2 u\left ( k \right )^{2}</math><br>  
 +
 
 +
----
 +
 
 +
'''Discussion:'''
 +
 
 +
This problem need a bit interpretation before getting the standard form to apply KKT condition.
 +
 
 +
Theorem about KKT, SONC, SOSC please see "Part 5".
 +
 
 +
----
  
 
<math>\color{blue}\text{Solution 1:}</math>  
 
<math>\color{blue}\text{Solution 1:}</math>  
  
 
<math>\left.\begin{matrix}  
 
<math>\left.\begin{matrix}  
x\left ( 1 \right )=2x\left ( 0 \right )+\mu\left ( 0\right )\\  
+
x\left ( 1 \right )=2x\left ( 0 \right )+u\left ( 0\right )\\  
x\left ( 2 \right )=2x\left ( 1 \right )+\mu\left ( 1\right )\\  
+
x\left ( 2 \right )=2x\left ( 1 \right )+u\left ( 1\right )\\  
x\left ( 3 \right )=2x\left ( 2 \right )+\mu\left ( 2\right )\\
+
x\left ( 3 \right )=2x\left ( 2 \right )+u\left ( 2\right )\\
 
x\left ( 0 \right )=0
 
x\left ( 0 \right )=0
 
\end{matrix}\right\} \Rightarrow  
 
\end{matrix}\right\} \Rightarrow  
 
\left\{\begin{matrix}
 
\left\{\begin{matrix}
x\left ( 1 \right )=\mu\left ( 0 \right )\\  
+
x\left ( 1 \right )=u\left ( 0 \right )\\  
x\left ( 2 \right )=2\mu\left ( 0 \right )+\mu\left ( 1\right )\\  
+
x\left ( 2 \right )=2u\left ( 0 \right )+u\left ( 1\right )\\  
x\left ( 3 \right )=4\mu\left ( 0 \right )+2\mu\left ( 1\right )+\mu\left ( 2 \right )=7
+
x\left ( 3 \right )=4u\left ( 0 \right )+2u\left ( 1\right )+u\left ( 2 \right )=7
 
\end{matrix}\right.</math><br>  
 
\end{matrix}\right.</math><br>  
  
<math>\text{The problem is equivalent to minimize }</math>
+
<font face="serif"><math>\text{The problem is equivalent to minimize } J=\frac{1}{2}\sum\limits_{k=0}^2 u\left ( k \right )^{2}</math><br></font>  
  
<br>  
+
<font face="serif"></font>&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{subject to } 4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)=7</math>
 +
 
 +
<math>\text{Let } h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7</math><br>
 +
 
 +
<math>\text{FONC: } \left\{\begin{matrix}
 +
l\left(u,\lambda \right)=\nabla J\left ( u \right )+\lambda\nabla h\left( u \right)=\begin{pmatrix}
 +
u\left ( 0 \right )\\
 +
u\left ( 1 \right )\\
 +
u\left ( 2 \right )
 +
\end{pmatrix}+\lambda\begin{pmatrix}
 +
4\\
 +
2\\
 +
1
 +
\end{pmatrix} =0\\
 +
h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7=0
 +
\end{matrix}\right.
 +
\Rightarrow
 +
\left\{\begin{matrix}
 +
u\left(0 \right)=\frac{4}{3}\\
 +
u\left(1 \right)=\frac{2}{3}\\
 +
u\left(2 \right)=\frac{1}{3}\\
 +
\lambda=-\frac{1}{3}
 +
\end{matrix}\right.</math>
 +
 
 +
<math>\text{SOSC: } L\left( u,\lambda  \right)=\nabla l\left( u,\lambda \right)=\begin{bmatrix}
 +
1 & 0 & 0\\
 +
0 & 1 & 0\\
 +
0 & 0 & 1
 +
\end{bmatrix}>0</math>
 +
 
 +
The sequence &nbsp;<math>u\left( 0 \right)=\frac{4}{3},u\left( 1 \right)=\frac{2}{3},u\left( 2 \right)=\frac{1}{3}</math>&nbsp; satisfies SOSC. It is the optimal sequence.
 +
 
 +
<math>\color{red} \text{This solution did not specifically state the complete SOSC.}</math><br>  
  
 
----
 
----
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<math>\color{blue}\text{Solution 2:}</math>  
 
<math>\color{blue}\text{Solution 2:}</math>  
  
<math>x\left ( 1 \right )=\mu\left ( 0 \right )</math><br>  
+
<math>x\left ( 1 \right )=u\left ( 0 \right )</math><br>  
  
<math>x\left ( 2 \right )=2\mu\left ( 0 \right )+\mu\left ( 1 \right )</math>  
+
<math>x\left ( 2 \right )=2u\left ( 0 \right )+u\left ( 1 \right )</math>  
  
<math>x\left ( 3 \right )=4\mu\left ( 0 \right )+2\mu\left ( 1\right )+\mu\left ( 2 \right )=7</math>  
+
<math>x\left ( 3 \right )=4u\left ( 0 \right )+2u\left ( 1\right )+u\left ( 2 \right )=7</math>  
  
<math>\text{The problem transfer to min } J\left ( \mu \right )=\frac{1}{2} \mu \left ( 0 \right )^{2}+\frac{1}{2} \mu \left ( 1 \right )^{2}+\frac{1}{2} \mu \left ( 2 \right )^{2}</math>  
+
<math>\text{The problem transfer to min } J\left ( u \right )=\frac{1}{2} u \left ( 0 \right )^{2}+\frac{1}{2} u \left ( 1 \right )^{2}+\frac{1}{2} u \left ( 2 \right )^{2}</math>  
  
<math>\text{subject to } h(\mu )=4\mu \left(0 \right)+2\mu \left(1 \right)+\mu\left(2 \right)-7=0</math>  
+
&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{subject to } h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7=0</math>  
  
<math>\text{Apply KKT condition: } Dl\left( \mu ,\lambda \right)=DJ\left(\mu \right)+\lambda Dh\left(\mu \right)=\left[ \mu\left(0  \right)+4\lambda,\mu\left(1  \right)+2\lambda,\mu\left(2  \right)+\lambda \right]=0</math>  
+
<math>\text{Apply KKT condition: } Dl\left( u ,\lambda \right)=DJ\left(u \right)+\lambda Dh\left(u \right)=\left[ u\left(0  \right)+4\lambda,u\left(1  \right)+2\lambda,u\left(2  \right)+\lambda \right]=0</math>  
  
<math>\left\{\begin{matrix}
+
&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>\left\{\begin{matrix}
\mu\left(0 \right)+4\lambda=0\\  
+
u\left(0 \right)+4\lambda=0\\  
\mu\left(1 \right)+2\lambda=0\\  
+
u\left(1 \right)+2\lambda=0\\  
\mu\left(2 \right)+\lambda=0\\  
+
u\left(2 \right)+\lambda=0\\  
4\mu\left(0 \right)+2\mu\left(1 \right)+\mu\left(2 \right)-7=0
+
4u\left(0 \right)+2u\left(1 \right)+u\left(2 \right)-7=0
 
\end{matrix}\right.
 
\end{matrix}\right.
 
\Rightarrow  
 
\Rightarrow  
 
\left\{\begin{matrix}
 
\left\{\begin{matrix}
\mu\left(0 \right)=\frac{4}{3}\\  
+
u\left(0 \right)=\frac{4}{3}\\  
\mu\left(1 \right)=\frac{2}{3}\\  
+
u\left(1 \right)=\frac{2}{3}\\  
\mu\left(2 \right)=\frac{1}{3}\\  
+
u\left(2 \right)=\frac{1}{3}\\  
 
\lambda=-\frac{1}{3}
 
\lambda=-\frac{1}{3}
 
\end{matrix}\right.</math>  
 
\end{matrix}\right.</math>  
  
<math>\text{Check SOSC: } L\left( \mu,\lambda  \right)=D^{2}l\left( \mu,\lambda \right)=\begin{bmatrix}
+
<math>\text{Check SOSC: } L\left( u,\lambda  \right)=D^{2}l\left( u,\lambda \right)=\begin{bmatrix}
 
1 & 0 & 0\\  
 
1 & 0 & 0\\  
 
0 & 1 & 0\\  
 
0 & 1 & 0\\  
Line 72: Line 123:
  
 
<math>\therefore \text{sequence } \left\{ \frac{4}{3},\frac{2}{3},\frac{1}{3} \right\} \text{ satisfy SOSC is a strict minimizer of the problem.}</math>  
 
<math>\therefore \text{sequence } \left\{ \frac{4}{3},\frac{2}{3},\frac{1}{3} \right\} \text{ satisfy SOSC is a strict minimizer of the problem.}</math>  
 +
 +
----
 +
----
 +
<math>\color{blue} \text{Related Problem:}</math>
 +
 +
<span class="texhtml">extremize 9''x''<sub>1</sub> + 3''x''<sub>2</sub></span>
 +
 +
<math>\text{subject to } \frac{1}{2}x_{1}^{2}-x_{2}=0</math>
 +
 +
<math>\color{blue} \text{(i) Find point(s) that satisfy the FONC}</math>
 +
 +
<math>\color{blue} \text{(ii) Apply the SOSC to determine the nature of the critical point(s) from the previous part}</math>
 +
 +
<math>\color{blue} \text{Solution: }</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>l\left( x,\lambda \right)=9x_{1}+3x_{2}+\lambda\left( \frac{1}{2}x_{1}^{2}-x_{2} \right)</math>
 +
 +
Applying the FONC:
 +
 +
<math>D_{x} l\left( x,\lambda \right) = \begin{bmatrix}
 +
9+\lambda x_{1} & 3-\lambda
 +
\end{bmatrix} =\begin{bmatrix}
 +
0 & 0
 +
\end{bmatrix}</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>\Rightarrow \lambda^{*}=3 \text{ and }  x^{*}_{1}=-3</math>
 +
 +
<font color="#ff0000"><span style="font-size: 17px;">'''&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<math>\Rightarrow  x_{2}^{*}=0.5x_{1}^{*2}=9/2</math>'''</span></font>
 +
 +
'''<math>\therefore  \text{the point that satisfies the FONC is } x^{*}= \begin{bmatrix}
 +
-3\\
 +
9/2
 +
\end{bmatrix}</math> '''
 +
 +
Check SOSC:
 +
 +
<math>D_{x} L\left( x^{*},\lambda^{*} \right) = F\left( x^{*} \right)+  \lambda^{*} H\left( x^{*} \right) = \begin{bmatrix}
 +
3 & 0\\
 +
0 & 0
 +
\end{bmatrix}</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>T\left( x^{* }\right)= \left \{ y: \begin{bmatrix}
 +
-3 & -1
 +
\end{bmatrix}y=0  \right \} = \left \{ y =\begin{bmatrix}
 +
a & -3a
 +
\end{bmatrix}^{T}, a\in\Re  \right \}</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>\color{green} \text{For SOSC, we consider } \tilde{T}\left( x^{* },\mu^{*} \right) \text{, but here } \mu^{*} \text{ is not applicable.}</math>
 +
 +
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<math>\text{Hence } y^{T}L\left ( x^{\ast },\mu ^{\ast } \right )y=\begin{bmatrix}
 +
a & -3a
 +
\end{bmatrix}\begin{bmatrix}
 +
3 & 0\\
 +
0 & 0
 +
\end{bmatrix}\begin{bmatrix}
 +
a \\
 +
-3a
 +
\end{bmatrix}=3a^{2}>0</math>
 +
 +
Therefore, x* is a strict local minimizer.
  
 
<br>  
 
<br>  
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----
 
----
  
[[ECE PhD Qualifying Exams|Back to ECE Qualifying Exams (QE) page]]  
+
Automatic Control (AC)- Question 3, August 2011
 +
 
 +
Go to
 +
 
 +
*Problem 1: [[ECE-QE AC3-2011 solusion-1|solutions and discussions]]
 +
*Problem 2: [[ECE-QE AC3-2011 solusion-2|solutions and discussions]]
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*Problem 3: [[ECE-QE AC3-2011 solusion-3|solutions and discussions]]
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*Problem 4: [[ECE-QE_AC3-2011_solusion-4|solutions and discussions]]
 +
*Problem 5: [[ECE-QE AC3-2011 solusion-5|solutions and discussions]]
 +
 
 +
----
  
[[Category:ECE]] [[Category:QE]] [[Category:Automatic_Control]] [[Category:Problem_solving]]
+
[[ECE PhD Qualifying Exams|Back to ECE Qualifying Exams (QE) page]]

Latest revision as of 10:10, 13 September 2013


ECE Ph.D. Qualifying Exam in "Automatic Control" (AC)

Question 3, August 2011, Part 4

Part 1,2,3,4,5

 $ \color{blue}\text{4. } \left( \text{20 pts} \right) \text{ Consider the following model of a discrete-time system, } $

                    $ x\left ( k+1 \right )=2x\left ( k \right )+u\left ( k \right ), x\left ( 0 \right )=0, 0\leq k\leq 2 $

$ \color{blue}\text{Use the Lagrange multiplier approach to calculate the optimal control sequence} $

                   $ \left \{ u\left ( 0 \right ),u\left ( 1 \right ), u\left ( 2 \right ) \right \} $

$ \color{blue}\text{that transfers the initial state } x\left( 0 \right) \text{ to } x\left( 3 \right)=7 \text{ while minimizing the performance index} $
                   $ J=\frac{1}{2}\sum\limits_{k=0}^2 u\left ( k \right )^{2} $


Discussion:

This problem need a bit interpretation before getting the standard form to apply KKT condition.

Theorem about KKT, SONC, SOSC please see "Part 5".


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

$ \left.\begin{matrix} x\left ( 1 \right )=2x\left ( 0 \right )+u\left ( 0\right )\\ x\left ( 2 \right )=2x\left ( 1 \right )+u\left ( 1\right )\\ x\left ( 3 \right )=2x\left ( 2 \right )+u\left ( 2\right )\\ x\left ( 0 \right )=0 \end{matrix}\right\} \Rightarrow \left\{\begin{matrix} x\left ( 1 \right )=u\left ( 0 \right )\\ x\left ( 2 \right )=2u\left ( 0 \right )+u\left ( 1\right )\\ x\left ( 3 \right )=4u\left ( 0 \right )+2u\left ( 1\right )+u\left ( 2 \right )=7 \end{matrix}\right. $

$ \text{The problem is equivalent to minimize } J=\frac{1}{2}\sum\limits_{k=0}^2 u\left ( k \right )^{2} $

                                                                  $ \text{subject to } 4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)=7 $

$ \text{Let } h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7 $

$ \text{FONC: } \left\{\begin{matrix} l\left(u,\lambda \right)=\nabla J\left ( u \right )+\lambda\nabla h\left( u \right)=\begin{pmatrix} u\left ( 0 \right )\\ u\left ( 1 \right )\\ u\left ( 2 \right ) \end{pmatrix}+\lambda\begin{pmatrix} 4\\ 2\\ 1 \end{pmatrix} =0\\ h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7=0 \end{matrix}\right. \Rightarrow \left\{\begin{matrix} u\left(0 \right)=\frac{4}{3}\\ u\left(1 \right)=\frac{2}{3}\\ u\left(2 \right)=\frac{1}{3}\\ \lambda=-\frac{1}{3} \end{matrix}\right. $

$ \text{SOSC: } L\left( u,\lambda \right)=\nabla l\left( u,\lambda \right)=\begin{bmatrix} 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1 \end{bmatrix}>0 $

The sequence  $ u\left( 0 \right)=\frac{4}{3},u\left( 1 \right)=\frac{2}{3},u\left( 2 \right)=\frac{1}{3} $  satisfies SOSC. It is the optimal sequence.

$ \color{red} \text{This solution did not specifically state the complete SOSC.} $


$ \color{blue}\text{Solution 2:} $

$ x\left ( 1 \right )=u\left ( 0 \right ) $

$ x\left ( 2 \right )=2u\left ( 0 \right )+u\left ( 1 \right ) $

$ x\left ( 3 \right )=4u\left ( 0 \right )+2u\left ( 1\right )+u\left ( 2 \right )=7 $

$ \text{The problem transfer to min } J\left ( u \right )=\frac{1}{2} u \left ( 0 \right )^{2}+\frac{1}{2} u \left ( 1 \right )^{2}+\frac{1}{2} u \left ( 2 \right )^{2} $

                                             $ \text{subject to } h(u )=4u \left(0 \right)+2u \left(1 \right)+u\left(2 \right)-7=0 $

$ \text{Apply KKT condition: } Dl\left( u ,\lambda \right)=DJ\left(u \right)+\lambda Dh\left(u \right)=\left[ u\left(0 \right)+4\lambda,u\left(1 \right)+2\lambda,u\left(2 \right)+\lambda \right]=0 $

                                                      $ \left\{\begin{matrix} u\left(0 \right)+4\lambda=0\\ u\left(1 \right)+2\lambda=0\\ u\left(2 \right)+\lambda=0\\ 4u\left(0 \right)+2u\left(1 \right)+u\left(2 \right)-7=0 \end{matrix}\right. \Rightarrow \left\{\begin{matrix} u\left(0 \right)=\frac{4}{3}\\ u\left(1 \right)=\frac{2}{3}\\ u\left(2 \right)=\frac{1}{3}\\ \lambda=-\frac{1}{3} \end{matrix}\right. $

$ \text{Check SOSC: } L\left( u,\lambda \right)=D^{2}l\left( u,\lambda \right)=\begin{bmatrix} 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1 \end{bmatrix}>0 $

$ \therefore \text{For all y, } y^{T}Ly\geq 0 $

$ \therefore \text{sequence } \left\{ \frac{4}{3},\frac{2}{3},\frac{1}{3} \right\} \text{ satisfy SOSC is a strict minimizer of the problem.} $



$ \color{blue} \text{Related Problem:} $

extremize 9x1 + 3x2

$ \text{subject to } \frac{1}{2}x_{1}^{2}-x_{2}=0 $

$ \color{blue} \text{(i) Find point(s) that satisfy the FONC} $

$ \color{blue} \text{(ii) Apply the SOSC to determine the nature of the critical point(s) from the previous part} $

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

        $ l\left( x,\lambda \right)=9x_{1}+3x_{2}+\lambda\left( \frac{1}{2}x_{1}^{2}-x_{2} \right) $

Applying the FONC:

$ D_{x} l\left( x,\lambda \right) = \begin{bmatrix} 9+\lambda x_{1} & 3-\lambda \end{bmatrix} =\begin{bmatrix} 0 & 0 \end{bmatrix} $

              $ \Rightarrow \lambda^{*}=3 \text{ and } x^{*}_{1}=-3 $

           $ \Rightarrow x_{2}^{*}=0.5x_{1}^{*2}=9/2 $

$ \therefore \text{the point that satisfies the FONC is } x^{*}= \begin{bmatrix} -3\\ 9/2 \end{bmatrix} $

Check SOSC:

$ D_{x} L\left( x^{*},\lambda^{*} \right) = F\left( x^{*} \right)+ \lambda^{*} H\left( x^{*} \right) = \begin{bmatrix} 3 & 0\\ 0 & 0 \end{bmatrix} $

              $ T\left( x^{* }\right)= \left \{ y: \begin{bmatrix} -3 & -1 \end{bmatrix}y=0 \right \} = \left \{ y =\begin{bmatrix} a & -3a \end{bmatrix}^{T}, a\in\Re \right \} $

              $ \color{green} \text{For SOSC, we consider } \tilde{T}\left( x^{* },\mu^{*} \right) \text{, but here } \mu^{*} \text{ is not applicable.} $

              $ \text{Hence } y^{T}L\left ( x^{\ast },\mu ^{\ast } \right )y=\begin{bmatrix} a & -3a \end{bmatrix}\begin{bmatrix} 3 & 0\\ 0 & 0 \end{bmatrix}\begin{bmatrix} a \\ -3a \end{bmatrix}=3a^{2}>0 $

Therefore, x* is a strict local minimizer.



Automatic Control (AC)- Question 3, August 2011

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