Practice Homework 3 - Optimization | MATH 0164, Assignments of Optimization Techniques in Engineering

Material Type: Assignment; Class: OPTIMIZATION; Subject: Mathematics; University: University of California - Los Angeles; Term: Winter 2005;

Typology: Assignments

Pre 2010

Uploaded on 08/31/2009

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Math 164, Lecture 2, Vese
Homework #3, due on Friday, January 27, 2005
Remarks:
Please note that exceptionally, there is no office hour with the instructor
on Wednesday, January 25.
Review Sections 2.3 (except 2.3.1) and 3.1 from the textbook.
[1] Prove that a function fis concave if and only if fis convex.
[2] Let fbe a convex function on the convex set Sof Rn. Let kbe a nonzero
scalar, and define g(x) = kf (x). Prove that if k > 0 then gis a convex
function on S, and if k < 0 then gis a concave function of S.
[3] Consider a feasible region Sdefined by a set of linear constraints
S={x:Ax b},
where Ais an m×nmatrix and bis a column vector.
(a) Prove that Sis convex.
(b) Derive the conditions that must be satisfied by a feasible direction p
at a point xS.
[4] Consider the problem
minimize f(x)
subject to x1+ 2x2+ 3x3= 6, x10, x20, x30.
(a) Find the sets of all feasible directions at points xa= (0,0,2)T,xb=
(3,0,1)T, and xc= (1,1,1)T.
(b) Using (a), verify that p= (3,0,1)Tis a feasible direction for xc=
(1,1,1)T; then find an upper bound on the step length αso that xc+αp is
a feasible point, with p= (3,0,1)T.
1

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Math 164, Lecture 2, Vese Homework #3, due on Friday, January 27, 2005

Remarks:

  • Please note that exceptionally, there is no office hour with the instructor on Wednesday, January 25.
  • Review Sections 2.3 (except 2.3.1) and 3.1 from the textbook.

[1] Prove that a function f is concave if and only if −f is convex.

[2] Let f be a convex function on the convex set S of Rn. Let k be a nonzero scalar, and define g(x) = kf (x). Prove that if k > 0 then g is a convex function on S, and if k < 0 then g is a concave function of S.

[3] Consider a feasible region S defined by a set of linear constraints

S = {x : Ax ≤ b},

where A is an m × n matrix and b is a column vector. (a) Prove that S is convex. (b) Derive the conditions that must be satisfied by a feasible direction p at a point x ∈ S.

[4] Consider the problem minimize f (x) subject to x 1 + 2x 2 + 3x 3 = 6, x 1 ≥ 0 , x 2 ≥ 0 , x 3 ≥ 0. (a) Find the sets of all feasible directions at points xa = (0, 0 , 2)T^ , xb = (3, 0 , 1)T^ , and xc = (1, 1 , 1)T^. (b) Using (a), verify that p = (3, 0 , −1)T^ is a feasible direction for xc = (1, 1 , 1)T^ ; then find an upper bound on the step length α so that xc + αp is a feasible point, with p = (3, 0 , −1)T^.