Optimization - Homework 2 Practice | MATH 0164, Assignments of Optimization Techniques in Engineering

Material Type: Assignment; Class: OPTIMIZATION; Subject: Mathematics; University: University of California - Los Angeles; Term: Winter 2006;

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Pre 2010

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Math 164, Lecture 2
Homework #2, due on Friday, January 20, 2006
(no late homework accepted)
Please solve as many problems as you can from the textbook. Also, please
review the material in the Appendices, at the end of the textbook, and read
Sections 2.2. and 2.3 from the textbook.
[1] Consider the feasible region defined by the constraints
1(x1)2(x2)20,1x1x20,and x20.
For each of the following points, determine whether the point is feasible or
infeasible, and (if it is feasible) whether it is interior to or on the boundary
of each of the constraints:
xa= (1
2,1
2)T, xb= (1,1)T, xc= (1,0)T, xd= (1
2,0)T, xe= ( 1
2,1
2)T.
[2] Consider the problem
minimize x1
subject to (x1)2+ (x2)24,
(x1)21.
Graph the feasible set. Use the graph to find all local minimizers for the
problem, and determine which of those are also global minimizers.
[3] Let S1={x= (x1, x2) : x1+x21, x10}, and S2={x=
(x1, x2) : x1x20, x11}, and let S=S1S2. Prove that S1and S2
are both convex sets, but that Sis not a convex set.
(this shows that the union of convex sets is not necessarily convex, but
the intersection is, see exercise 1 page 24).
[4] Let f:RnRbe a convex function, and let g:RRbe a convex
nondecreasing function. Prove that the composite function h:RnR
defined by h(x) = g(f(x)) is convex.
[5] Consider the one-variable function
f(x) = (x+ 1)x(x2)(x5) = x46x3+ 3x2+ 10x.
Graph this function and locate (approximately) the stationary points, local
minima, and global minima.
1

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Math 164, Lecture 2 Homework #2, due on Friday, January 20, 2006 (no late homework accepted)

Please solve as many problems as you can from the textbook. Also, please review the material in the Appendices, at the end of the textbook, and read Sections 2.2. and 2.3 from the textbook.

[1] Consider the feasible region defined by the constraints

1 − (x 1 )^2 − (x 2 )^2 ≥ 0 , 1 − x 1 − x 2 ≥ 0 , and x 2 ≥ 0.

For each of the following points, determine whether the point is feasible or infeasible, and (if it is feasible) whether it is interior to or on the boundary of each of the constraints:

xa = (

)T^ , xb = (1, 1)T^ , xc = (− 1 , 0)T^ , xd = (−

, 0)T^ , xe = (

)T^.

[2] Consider the problem minimize x 1 subject to (x 1 )^2 + (x 2 )^2 ≤ 4, (x 1 )^2 ≥ 1. Graph the feasible set. Use the graph to find all local minimizers for the problem, and determine which of those are also global minimizers.

[3] Let S 1 = {x = (x 1 , x 2 ) : x 1 + x 2 ≤ 1 , x 1 ≥ 0 }, and S 2 = {x = (x 1 , x 2 ) : x 1 − x 2 ≥ 0 , x 1 ≤ 1 }, and let S = S 1 ∪ S 2. Prove that S 1 and S 2 are both convex sets, but that S is not a convex set. (this shows that the union of convex sets is not necessarily convex, but the intersection is, see exercise 1 page 24).

[4] Let f : Rn^ → R be a convex function, and let g : R → R be a convex nondecreasing function. Prove that the composite function h : Rn^ → R defined by h(x) = g(f (x)) is convex.

[5] Consider the one-variable function

f (x) = (x + 1)x(x − 2)(x − 5) = x^4 − 6 x^3 + 3x^2 + 10x.

Graph this function and locate (approximately) the stationary points, local minima, and global minima.