Revision as of 21:40, 28 June 2012 by Hu45 (Talk | contribs)

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

Question 3, Part 2, August 2011

Part 1,2,3,4,5

 $ \color{blue}\text{5. } \left( \text{20 pts} \right) \text{ Consider the following optimization problem, } $

                            $ \text{optimize} \left(x_{1}-2\right)^{2}+\left(x_{2}-1\right)^{2} $

                        $ \text{subject to } x_{2}- x_{1}^{2}\geq0 $

                                                 $ 2-x_{1}-x_{2}\geq0 $

                                                 $ x_{1}\geq0. $

$ \color{blue} \text{The point } x^{*}=\begin{bmatrix} 0 & 0 \end{bmatrix}^{T} \text{ satisfies the KKT conditions.} $


Theorem:

For the problem:    minimize  $ f \left( x \right) $

                             subject to   $ h \left( x \right) =0 $

                                                $ g \left( x \right) \leq 0 $

The KKT condition (FONC) is:

$ \text{1. } \mu^{*}\geq0 $

$ \text{2. } Df\left ( x^{*} \right )+\lambda ^{*T}Dh\left ( x^{*} \right )+\mu ^{*T}Dg\left ( x^{*} \right )=0^{T} $

$ \text{3. } \mu ^{*T}g\left ( x^{*} \right )=0 $

$ \text{4. } h\left ( x^{*} \right )=0 $

$ \text{5. } g \left( x^{*} \right) \leq0 $


$ \color{blue}\left( \text{i} \right) \text{Does } x^{*} \text{ satisfy the FONC for minimum or maximum? Where are the KKT multipliers?} $

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

        $ f\left( x \right) = \left(x_{1}-2\right)^{2}+\left(x_{2}-1\right)^{2} $

        $ g_{1}\left( x \right)=x_{1}^{2}-x_{2} $

        $ g_{2}\left( x \right)= x_{1}+x_{2}-2 $

        $ g_{3}\left( x \right)= -x_{1} $

 $ \text{ The problem is to optimize f(x), subject to } g_{1}\leq 0, g_{2}\leq 0, g_{3}\leq 0 $

$ \text{Let } l\left( \mu ,\lambda \right)=\nabla f\left(x \right)+\mu_{1} \nabla g_{1}\left( x \right)+\mu_{2} \nabla g_{2}\left( x \right)+\mu_{3} \nabla g_{3}\left( x \right) $

                      $ =\begin{pmatrix} 2x_{1}-4\\ 2x_{2}-2 \end{pmatrix} +\mu_{1} \begin{pmatrix} 2x_{1}\\ -1 \end{pmatrix}+\mu_{2}+\begin{pmatrix} 1\\ 1 \end{pmatrix}+\mu_{3}+\begin{pmatrix} -1\\ 0 \end{pmatrix} =0 $

$ \mu_{1} g_{1}\left( x \right)+\mu_{2} g_{2}\left( x \right)+\mu_{3} g_{3}\left( x \right) $

            $ = \mu_{1} \left( x_{1}^2-x_{2} \right)+\mu_{2} \left( x_{1}+x_{2}-2 \right)+\mu_{3} \left( -x_{1} \right) =0 $

$ \text{Let } x^{*}=\begin{bmatrix} 0\\ 0 \end{bmatrix} \text{, } $

$ \left\{\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. $

$ \text{As } \mu^{*}\leq 0, x^{*}\begin{bmatrix} 0\\0 \end{bmatrix} \text{satisfies the FONC for maximum.} $


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

$ \text{ Standard form: optimize} \left(x_{1}-2\right)^{2}+\left(x_{2}-1\right)^{2} $

                                  $ \text{subject to } g_{1}\left( x \right)= x_{1}^{2}-x_{2}\leq0 $

                                                           $ g_{2}\left( x \right)= x_{1}+x_{2}-2\leq0 $

                                                           $ g_{3}\left( x \right)= -x_{1}\leq0 $

$ \text{KKT condition: (1) } Dl\left( \mu ,\lambda \right)=Df\left(x \right)+\mu_{1}Dg_{1}\left( x \right)+\mu_{2}Dg_{2}\left( x \right)+\mu_{3}Dg_{3}\left( x \right) $

                                                                      $ =\left [ 2x_{1}-4+2\mu_{1}x_{1}+\mu_{2}-\mu_{3}, 2x_{2}-2-\mu_{1}+\mu_{2} \right ]=0 $

                                        $ \left ( 2 \right ) \mu^{T}g\left ( x \right )=0 \Rightarrow \mu_{1}\left ( x_{1}^2-x_{2} \right )+\mu_{2}\left ( x_{1}+x_{2}-2 \right ) - \mu_{3}x_{1}=0 $

                                        $ \left ( 3 \right ) \mu_{1},\mu_{2},\mu_{3}\geq 0 \text{ for minimizer} $

                                               $ \mu_{1},\mu_{2},\mu_{3}\leq 0 \text{ for maximizer} $

                                        $ \text{where } \mu^{*}=\begin{bmatrix} \mu_{1}\\ \mu_{2}\\ \mu_{3} \end{bmatrix} \text{ are the KKT multiplier.} $

$ \text{For } x^{*}=\begin{bmatrix} 0\\ 0 \end{bmatrix} \text{, } $       $ \left\{\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. $

$ \therefore x^{*}=\begin{bmatrix} 0 \\ 0 \end{bmatrix} \text{ satisfy FONC for maximum} $


$ \color{blue}\left( \text{ii} \right) \text{Does } x^{*} \text{ satisfy SOSC? Carefully justify your answer.} $

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

$ L\left ( x^{*},\mu^{*} \right )= \nabla l \left( x^{*},\mu^{*} \right)= \begin{pmatrix} 2&0 \\ 0&2 \end{pmatrix}-2\begin{pmatrix} 2&0 \\ 0&0 \end{pmatrix} = \begin{pmatrix} -2&0 \\ 0&2 \end{pmatrix} $

Failed to parse (lexing error): \tilde{T}\left( x^{* }\mu^{*} \right) : \left\{ \begin{matrix} y^{T}\binom{0}{-1} =0 \\ y^{T}\binom{-1}{0} =0 \end{matrix} \right. \Rightarrow \tilde{T}\left( x^{* }\mu^{*} \right)= \left \{ \binom{0}{0} \right \}

SOSC is trivially satisfied.


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

$ L\left ( x_{1}\mu \right )= D^{2} l \left ( x _{1}\mu \right )= \begin{bmatrix} 2+2\mu_{1} & 0 \\ 0 & 2 \end{bmatrix} $

                   $ \text{for point } x^{*}=\begin{bmatrix} 0 \\ 0 \end{bmatrix} \text{, we get } \mu_{1}=-2 \text{ from KKT condition.} $

$ \therefore L \left ( x^{*}, \mu ^{*}\right )=\begin{bmatrix} -2 & 0 \\ 0 & 2 \end{bmatrix} $

$ \tilde{T}\left( x^{* }\mu^{*} \right)= \left \{ y:Dg_{i}\left( x^{*} \right)y=0, i\in \tilde{J}\left( x^{*},\mu^{*} \right) \right \} $

$ \tilde{J}\left ( x^{\ast },\mu ^{\ast } \right )= \left \{ i:g_{i}\left ( x^{\ast } \right ) = 0,\mu_{i}^{\ast }> 0\right \} $     $ \therefore i= 2 $

$ \therefore \tilde{T}\left ( x^{\ast },\mu ^{\ast } \right )= \left \{ y:\left [ 1,1 \right ]y= 0 \right \}= \left \{ y:y_{1}= -y_{2} \right \} $

$ \begin{bmatrix} y_{1}& y_{2} \end{bmatrix}\begin{bmatrix} -2 & 0\\ 0 & 2 \end{bmatrix} \begin{bmatrix} y_{1}\\ y_{2} \end{bmatrix} \geqslant 0 $

                    $ -2y_{1}^{2}+2y_{2}^{2}\geqslant 0\cdots \left ( 1 \right ) $

                    for y1 = y2,  (1) is always satisfied.

$ \therefore \text{For all } y\in T\left( x^{*} \right ) \text{, we have } y^{T}L\left ( x^{\ast },\mu ^{\ast } \right )y\geq 0 $

$ \therefore \text{point } x^{*} \text{satisfy the SOSC} $


Automatic Control (AC)- Question 3, August 2011

Go to


Back to ECE Qualifying Exams (QE) page

Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang