Difference between revisions of "QE2016 AC-3 ECE580" - Rhea

Latest revision as of 16:15, 19 February 2019

Automatic Control (AC)

Question 3: Optimization

August 2016

1.(20 pts) Considern the following linear program,

minimize

2x_{1} + x_{2}

,
subject to

x_{1} + 3x_{2} \geq 6

2x_{1} + x_{2} \geq 4

x_{1} + x_{2} \leq 3

x_{1} \geq 0

,

x_{2} \geq 0

.

Convert the above linear program into standard form and find an initial basic feasible solution for the program in standard form.

Click here to view student answers and discussions

2.(20 pts)

(15 pts) FInd the largest range of the step-size, $\alpha$ , for which the fixed step gradient descent algorithm is guaranteed to converge to the minimizer of the quadratic function

f = \frac{1}{2} x^{T}Qx - b^{T}x

starting from an arbitary initial condition $x^{(0)} \in \mathbb{R}^{n}$ , where $x \in \mathbb{R}^{n}$ , and $Q = Q^{T} > 0$ .

(5 pts) Find the largest range of the step size, $\alpha$ , for which the fixed step gradient descent algorithm is guaranteed to converge to the minimizer of the quadratic function

f= 6x_{1}^{2}+2x_{2}^{2}-5

starting from an arbitrary initial condition $x^{(0)} \in \mathbb{R}^{n}$

Click here to view student answers and discussions

3. (20 pts) Is the function

f(x_{1}, x_{2})=\frac{1}{(x_{1}-2)^{2} + (x_{2}+1)^{2}+3}

locally convex, concave, or neither in the neighborhood of the point $[2 -1]^{T}$ ? Justify your answer by giving all the details of your argument.

Click here to view student answers and discussions

4. (20 pts) Solve the following optimization problem:

optimize

x_{1}x_{2}

subject to

x_{1}+x_{2}+x_{3}=1

x_{1}+x_{2}-x_{3}=0

Click here to view student answers and discussions

5. (20 pts) Solve the following optimization problem:

maximize

14x_{1}-x_{1}^{2}+6x_{2}-x_{2}^{2}+7

subject to

x_{1}+x_{2} \leq 2

x_{1}+2x_{2} \leq 3

Click here to view student answers and discussions

Back to ECE QE page

@@ Line 21: / Line 21: @@
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-:Student answers and discussions for [[QE2013_AC-3_ECE580-1|Part 1]],[[QE2013_AC-3_ECE580-2|2]],[[QE2013_AC-3_ECE580-3|3]],[[QE2013_AC-3_ECE580-4|4]],[[QE2013_AC-3_ECE580-5|5]]
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-'''1.(20 pts) In some of the optimization methods, when minimizing a given function f(x), we select an intial guess <math>x^{(0)}</math> and a real symmetric positive definite matrix <math>H_{0}</math>.  Then we computed <math>d^{(k)} = -H_{k}g^{(k)}</math>, where <math>g^{(k)} = \nabla f( x^{(k)} )</math>, and <math>x^{(k+1)} = x^{(k)} + \alpha_{k}d^{(k)}</math>, where'''
+.(20 pts) Considern the following linear program, <br/>
-<br>
+<center> minimize <math>2x_{1} + x_{2}</math>, </center> <br/>
-<math> \alpha_{k} = arg\min_{\alpha \ge 0}f(x^{(k)} + \alpha d^{(k)}) .</math>
+<center> subject to <math>x_{1} + 3x_{2} \geq 6 </math> </center> <br/>
-<br>
+<center> <math>2x_{1} + x_{2} \geq 4</math> </center> <br/>
-'''Suppose that the function we wish to minimize is a standard quadratic of the form,'''
+<center> <math> x_{1} + x_{2} \leq 3 </math> </center> <br/>
-<br>
+<center> <math> x_{1} \geq 0 </math>, <math> x_{2} \geq 0 </math>. </center> <br/>
-<math> f(x) = \frac{1}{2} x^{T} Qx - x^{T}b+c, Q = Q^{T} > 0. </math>
+Convert the above linear program into standard form and find an initial basic feasible solution for the program in standard form. <br/>
-<br><br>
+:'''Click [[2016AC-3-1|here]] to view student [[2016AC-3-1|answers and discussions]]'''
-'''(i)(10 pts) Find a closed form expression for <math>\alpha_k</math> in terms of <math>Q, H_k, g^{(k)}</math>, and  <math>d^{(k)}; </math>'''
+----
-<br>
+.(20 pts)
-'''(ii)(10 pts) Give a sufficient condition on <math>H_k</math> for <math>\alpha_k</math> to be positive.'''
+*(15 pts) FInd the largest range of the step-size, <math> \alpha </math>, for which the fixed step gradient descent algorithm is guaranteed to converge to the minimizer of the quadratic function <br/>
+<center> <math> f = \frac{1}{2} x^{T}Qx - b^{T}x </math> </center> <br/>
+starting from an arbitary initial condition <math> x^{(0)} \in \mathbb{R}^{n} </math>, where  <math> x \in \mathbb{R}^{n} </math>, and <math>Q = Q^{T} > 0</math>. <br/>
+*(5 pts) Find the largest range of the step size, <math>\alpha</math>, for which the fixed step gradient descent algorithm is guaranteed to converge to the minimizer of the quadratic function<br/>
+<center><math> f= 6x_{1}^{2}+2x_{2}^{2}-5 </math></center> <br/>
+starting from an arbitrary initial condition <math>x^{(0)} \in \mathbb{R}^{n}</math>
+:'''Click [[2016AC-3-2|here]] to view student [[2016AC-3-2|answers and discussions]]'''
-:'''Click [[QE2013_AC-3_ECE580-1|here]] to view [[QE2013_AC-3_ECE580-1|student answers and discussions]]'''
 ----
+. (20 pts) Is the function <br/>
+<center><math> f(x_{1}, x_{2})=\frac{1}{(x_{1}-2)^{2} + (x_{2}+1)^{2}+3} </math></center><br>
+locally convex, concave, or neither in the neighborhood of the point <math> [2 -1]^{T} </math>? Justify your answer by giving all the details of your argument.
+:'''Click [[2016AC-3-3|here]] to view student [[2016AC-3-3|answers and discussions]]'''
 ----
-[[ECE_PhD_Qualifying_Exams|Back to ECE QE page]]
+. (20 pts) Solve the following optimization problem:
+<center>optimize <math> x_{1}x_{2} </math> </center><br>
+<center>subject to <math> x_{1}+x_{2}+x_{3}=1 </math> </center><br>
+<center><math> x_{1}+x_{2}-x_{3}=0 </math></center><br>
+:'''Click [[2016AC-3-4|here]] to view student [[2016AC-3-4|answers and discussions]]'''
+----
+. (20 pts) Solve the following optimization problem:<br/>
+<center>maximize <math> 14x_{1}-x_{1}^{2}+6x_{2}-x_{2}^{2}+7 </math></center><br>
+<center>subject to <math>x_{1}+x_{2} \leq 2</math></center><br>
+<center><math> x_{1}+2x_{2} \leq 3 </math></center>
+:'''Click [[2016AC-3-5|here]] to view student [[2016AC-3-5|answers and discussions]]'''
+----
+----
-[[ ECE PhD Qualifying Exams|Back to ECE PhD Qualifying Exams]]
+[[ECE_PhD_Qualifying_Exams|Back to ECE QE page]]

Difference between revisions of "QE2016 AC-3 ECE580" - Rhea

Latest revision as of 16:15, 19 February 2019

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