EEEN40580 Optimisation Techniques SEMESTER I EXAMINATIONS - 2017/2018
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SEMESTER I EXAMINATIONS - 2017/2018
EEEN40580 Optimisation Techniques
Section A: Short Questions
A1. Is the LP given by
min 2x1 + x2 +2x4
s.t. x1 +3x3 5, x1 +3x2 ≥ 3, x1 一 2x4 ≥ 4, x1 , x2 , x3 , x4 ≥ 0,
a) infeasible, or
b) unbounded, or
c) has an optimal solution ?
A2. Consider an integer-linear program (P) and its standard LP relaxation
(P(˜)). Assume we know that (P) has an optimal solution. Is (P(˜))
a) infeasible, or
b) unbounded, or
c) has an optimal solution ?
A3. Let (P) and (D) a primal-dual pair of LP. Assume we know (D) is infea- sible. Is (P)
a) infeasible, or
b) unbounded, or
c) has an optimal solution ?
A4. Let f(x) be a function of a real variable x e R. Which of the following statements holds true:
a) f is concave in R if and only iff(y) 一 f(x) (x)(y 一 x), Ax, y e R b) f is convex atx if and only if = 0.
c) f is concave atx if and only if dx(df) > 0 and dx(d2) < 0.
A5. Let f(x) be a function of a real vector x e Rn. Define Δf(x) := [ax1 f(x) ... axnf(x)]> , the gradient of the function f at point x. Which of the following state- ments holds true:
a) f is concave if and only if f(y) 一 f(x) P axif(x)(yi 一 xi ) for all x, y e Rn.
b) f attains a maximum at x e Rn if Δf(x) = 0.
c) f is concave at x e Rn if H(f)(x) is a negative semidefinite matrix.
A6. Let f(x) =P |xi | . Which of the following statements hold true:
a) f is di↵erentiable at x = 0 and its derivative is 0.
b) f is not di↵erentiable at x = 0 and its sub-gradient equals {x : maxi |xi | 三 1}.
c) f is not di↵erentiable at x = 0 and its sub-gradient equals 0.
Section B: Bullet-Point Questions
B1. Consider the optimization problem:
min cTx
s.t. aTx b for all a e E = {¯(a) + Pu | IuI2 1} ,
where c, ¯(a) e Rn , b e R, P e Rn 根n with P 0. Determine if this problem
is a Linear Program. If yes, provide an explanation why. If not, explain why not.
B2. Consider the optimization problem:
min cTx
s.t. aTx b for all a e E = {¯(a) + Pu | IuI2 1} ,
where c, ¯(a) e Rn , b e R, P e Rn 根n with P = 0. Determine if this problem
is a Linear Program. If yes, provide an explanation why. If not, explain why not.
B3. Consider the optimization problem:
min x1 + x2 + x3
s.t. x1 +4x2 2,
2x1 - x3 > -2,
3x2 - x3 ≥ -4,
x1 , x2 , x3 ≥ 0.
Determine if this problem is a Linear Program. If yes, provide an expla- nation why. If not, explain why not.
B4. Consider a linear program of the form:
max cTx
s.t. Ax = b.
Reformulate this LP as an equivalent LP, where all decision variables are required to be non-negative.
B5. State the definition for an optimization problem to be convex. Moreover, give a simple example for both, (a) a convex optimization problem, and
(b) a non-convex optimization problem.
B6. Given the integer linear program (ILP)
max cTx
s.t. Ax b,
x e Zn ,
where all entries of A and b are rational numbers.
State an linear program (LP), with the property that every optimal solu- tion to (ILP) is an optimal solution to (LP), and every optimal solution to (LP) that is an extreme point is an optimal solution to (ILP).
B7. Consider the primal-dual pair of LP
max (5, 3, 5)x
s.t. 0 3(1) 1(2) 2(−)1 1 x 0 4(2) 1
@ −1 1 1 A @ −1 A
and
min (2, 4, −1)y
s.t. 0 2(1) 1(3) 1(−)1 1 y = 0 3(5) 1 , y ≥ 0.
@ −1 2 1 A @ 5 A
Consider ¯(x)a = (1, −1, 1)T , ¯(x)b = (1.5, −2, 0)T , ¯(x)c = (−1, 1, 0)T and y(¯) = (0, 2, 1)T .
a) Check whether ¯(x)a , ¯(x)b and ¯(x)c feasible for the primal problem, and y(¯) feasible for the dual problem.
b) Check the complementary slackness conditions for (¯(x)a , y(¯)), (¯(x)b , y(¯)) and (¯(x)c , y(¯)), respectively.
c) Determine whether (¯(x)a , y(¯)), (¯(x)b , y(¯)) or (¯(x)c , y(¯)) constitutes a pair of op- timal solutions for the given primal-dual pair of LP.
B8. Define a set Q = {x = (x1 ... xn )> : P xi(4) 1}.
a) Is Q a convex set?
b) What is the minimax center of Q?
c) How many solutions does the following optimization problem have: minf(x) =P xi s.t. x 2 Q ?
d) Where is the minimal point of f(x) from c) attained, within the interior of Q or at the boundary of Q?
B9. What is the gradient descent method? What is it used for? Explain on an example in R1 .
B10. Assume x 2 X , X is a convex subset of Rn and fisa convex di↵erentiable function on Rn. What is the necessary and sufficent condition for x 2 X to be aminimum point off (formulate in terms of the variational inequality)? Provide an example in R2 .
B11. Define a local/global maximum of a function. How many local maxima does a concave function have? Provide an example of a concave function with multiple global maxima. Explain how to select a particular global maximum of a concave function by means of a regularisation.
B12. Explain with a simple example the di↵erence between a standard linear program, robust linear program and a robust stochastic linear program.
Section C: Longer Questions
C1. The director of the CO-Tech startup needs to decide what salaries too↵er its employees for the coming year. In order to keep the employees happy, she needs to satisfy the following constraints:
. Tom wants at least $20 000 or he will quit;
. Peter, Nina and Samir each want to be paid at least $5000 more than Tom;
. Gary wants his salary to be at least as high as the combined salary of Tom and Peter;
. Linda wants her salary to be $200 more than Gary;
. the combined salary of Nina and Samir should be at least twice the combined salary of Tom and Peter;
. Bob’s salary is at least as high as that of Peter and at least as high as that of Samir;
. the combined salary of Bob and Peter should be at least $60 000;
. Linda should not make more money than the combined salary of Bob and Tom.
a) Formulate as an LP that will determine the salaries for the employees of CO-Tech that satisfy each of the constraints while minimizing the total salary expenses.
b) Formulate as an LP that will determine the salaries for the employees of CO-Tech that satisfy each of the constraints while minimizing the salary of the highest paid employee.
C2. Consider the function f(x) = log (P eai xi ).
a) Is f convex? Compute the Hessian of f at point x.
b) Provide necessary and sufficient conditions for convexity of f at point x.
c) Write necessary and sufficient optimality conditions for x* to be a minimum point off in the cube [−1, 1]n.
d) Derive the gradient method to find x* from c).
e) Provide a condition on a1 ... an for x* to be the unique minimum point off in the cube [−1, 1]n.
2023-12-06