Math 164 Lecture 2: Linear Programming Homework 4, Assignments of Optimization Techniques in Engineering

Information about math 164 lecture 2, including homework assignment #4 with problems related to linear programming, minimization, feasible points, and feasible directions. Students are required to solve as many problems as they can from the textbook and review sections 4.2 and 4.3.

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Pre 2010

Uploaded on 08/26/2009

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Math 164, Lecture 2, Vese
Homework #4, due on Wednesday, October 26, 2005
Remarks:
Each time, please solve as many problems as you can from the textbook
Please review Sections 4.2 and 4.3 from the textbook.
[1] Consider the problem
minimize f(x)
subject to x1+ 2x2+ 3x3= 6, x10, x20, x30,
and the point xc= (1,1,1)T.
(a) Show that xcis a feasible point.
(b) Verify that p= (3,0,1)Tis a feasible direction for xc(see also your
previous homework).
(c) Find an upper bound on the step length αso that xc+αp is a feasible
point, with p= (3,0,1)T.
[2] Consider the following linear program (compare with Problem [3] from
Hw#3):
minimize
z= 3x1+x2,
subject to
x1+x2 1,
3x1+ 2x212,
2x1+ 3x23,
x1, x20.
(a) Solve the problem graphically.
(b) Write the problem in standard form.
[3] Consider the system of linear constraints
2x1+x2100,
x1+x280,
x140,
x1, x20.
(a) Write this system in standard form, and determine all the basic solu-
tions (feasible and infeasible).
(b) Determine the extreme points of the feasible region (corresponding to
both the standard form, as well as the original version).
1

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Math 164, Lecture 2, Vese Homework #4, due on Wednesday, October 26, 2005 Remarks:

  • Each time, please solve as many problems as you can from the textbook
  • Please review Sections 4.2 and 4.3 from the textbook.

[1] Consider the problem minimize f (x) subject to x 1 + 2x 2 + 3x 3 = 6, x 1 ≥ 0 , x 2 ≥ 0 , x 3 ≥ 0 ,

and the point xc = (1, 1 , 1)T^. (a) Show that xc is a feasible point. (b) Verify that p = (3, 0 , −1)T^ is a feasible direction for xc (see also your previous homework). (c) Find an upper bound on the step length α so that xc + αp is a feasible point, with p = (3, 0 , −1)T^.

[2] Consider the following linear program (compare with Problem [3] from Hw#3): minimize z = 3x 1 + x 2 , subject to −x 1 + x 2 ≥ − 1 , 3 x 1 + 2x 2 ≤ 12 , 2 x 1 + 3x 2 ≤ 3 , x 1 , x 2 ≥ 0. (a) Solve the problem graphically. (b) Write the problem in standard form.

[3] Consider the system of linear constraints

2 x 1 + x 2 ≤ 100 , x 1 + x 2 ≤ 80 , x 1 ≤ 40 , x 1 , x 2 ≥ 0. (a) Write this system in standard form, and determine all the basic solu- tions (feasible and infeasible). (b) Determine the extreme points of the feasible region (corresponding to both the standard form, as well as the original version).