<br> = [[ECE PhD Qualifying Exams|ECE Ph.D. Qualifying Exam]]: Automatic Control (AC)- Question 3, August 2011 = ---- &nbsp;<font color="#ff0000"><span style="font-size: 19px;"><math>\color{blue}\text{1. } \left( \text{20 pts} \right) \text{ Consider the optimization problem, }</math></span></font> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<math>\text{maximize} -x_{1}^{2}+x_{1}-x_{2}-x_{1}x_{2}</math> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<math>\text{subject to } x_{1}\geq0, x_{2}\geq0</math><font color="#ff0000" face="serif" size="4"><br></font> '''<math>\color{blue}\left( \text{i} \right) \text{ Characterize feasible directions at the point } x^{*}=\left[ \begin{array}{c} \frac{1}{2} \\ 0 \end{array} \right]</math>'''<br> ===== <math>\color{blue}\text{Solution 1:}</math> ===== <math>\text{We need to find a direction }d\text{, such that } \exists\alpha_{0}&gt;0,</math> <math>\left( \begin{array}{c} \frac{1}{2} \\ 0 \end{array} \right) + \alpha d \text{ for all } \alpha\in\Omega \left[0,\alpha_{0}\right]</math><br> <math>\text{As } x_{1}\geq0, x_{2}\geq0, d= \left( \begin{array}{c} x \\ y \end{array} \right)\text{where } x\in\Re, \text{ and } y\geq0.</math> ---- <math>\color{blue}\text{Solution 2:}</math> <math>d\in\Re_{2}, d\neq0 \text{ is a feasible direction at } x^{*} \text{, if } \exists \alpha_{0} \text{ that } \left[ \begin{array}{c} \frac{1}{2} \\ 0 \end{array} \right] + \alpha\left[ \begin{array}{c} d_{1} \\ d_{2} \end{array} \right] \in\Omega \text{ for all } 0\leq\alpha\leq\alpha_{0}</math>&nbsp;<br> '''<math>\because \begin{Bmatrix}x\in\Omega: x_{1}\geq0, x_{2}\geq0\end{Bmatrix}</math>''' <br> <math>\therefore d= \left[ \begin{array}{c} d_{1} \\ d_{2} \end{array} \right], d_{1}\in\Re^{2}, d_{2}\neq0</math><br> ---- <math>\color{blue}\left( \text{ii} \right) \text{Write down the second-order necessary condition for } x^{*} \text{. Does the point } x^{*} \text{ satisfy this condition?}</math><br> <math>\color{blue}\text{Solution 1:}</math> <font face="serif"><math>\text{It is equivalent to minimize } f\left(x\right) \text{, }</math>&nbsp;</font><math>\text{ subject to } g_{1}(x)\leq0, g_{2}(x)\leq0</math><font color="#ff0000">'''<br>'''</font> <math>l\left(x,\mu \right) = \nabla f(x)+\mu_{1}\nabla g_{1}(x)+ \mu_{2}\nabla g_{2}(x)=\left( \begin{array}{c} 2x_{1}-1+x_{2} \\ 1+x_{1} \end{array} \right) + \left( \begin{array}{c} -\mu_{1} \\ 0 \end{array} \right) +\left( \begin{array}{c} 0 \\ -\mu_{2} \end{array} \right) =0</math><br> <math>-\mu_{1}x_{1}-\mu_{2}x_{2}=0</math><br> ---- [[ECE PhD Qualifying Exams|Back to ECE Qualifying Exams (QE) page]] [[Category:ECE]] [[Category:QE]] [[Category:Automatic_Control]] [[Category:Problem_solving]]