Question 1

The computer time per iteration for an interior-point algorithm is approximately the same as for the simplex method.

Accepted Answer

A) True 
 B)False  False

Question 2

If some necessary constraints were not included in a linear programming model,it is possible to have no limit on the best objective function value.

Accepted Answer

A) True 
 B)False  True

Question 3

It is possible in some cases for a point inside the boundary of the feasible region to be an optimal solution.

Accepted Answer

A) True 
 B)False  False

Question 4

It is possible to have more than one corner point of the feasible region as an optimal solution to a linear programming problem.

Accepted Answer

A) True 
 B)False

Question 5

The simplex method is a specific type of interior-point algorithm.

Accepted Answer

A) True 
 B)False

Question 6

When choosing which corner point to advance to next,the simplex method chooses the one:

Accepted Answer

A) that is closest. 
B) along the edge with the largest rate of improvement. 
C) along the edge with a negative rate of improvement. 
D) that is furthest from the origin. 
E) None of the above. 
A) that is closest. 
B) along the edge with the largest rate of improvement. 
C) along the edge with a negative rate of improvement. 
D) that is furthest from the origin. 
E) None of the above.

Question 7

Interior-point algorithms have limited capability for what-if analysis.
Multiple Choice Questions

Accepted Answer

A) True 
 B)False

Question 8

If the feasible region in a linear programming problem is unbounded,then the problem is unbounded.

Accepted Answer

A) True 
 B)False

Question 9

There are no feasible solutions to a problem when the constraints are too restrictive.

Accepted Answer

A) True 
 B)False

Question 10

The simplex method can only solve problems with up to 100 functional constraints.

Accepted Answer

A) True 
 B)False

Question 11

The simplex method is which kind of algorithm?

Accepted Answer

A) iterative. 
B) repetitive. 
C) systematic. 
D) All of the above. 
E) None of the above. 
A) iterative. 
B) repetitive. 
C) systematic. 
D) All of the above. 
E) None of the above.

Question 12

In a linear programming problem,there is always at least one optimal corner point.

Accepted Answer

A) True 
 B)False

Question 13

The enumeration-of-corner-points method is limited because it can only solve problems with two decision variables.

Accepted Answer

A) True 
 B)False

Question 14

Whenever possible,the initialization step of the simplex method chooses the origin to be the initial corner point.

Accepted Answer

A) True 
 B)False

Question 15

If a linear programming problem does not have an optimal solution then it must be infeasible.

Accepted Answer

A) True 
 B)False

Question 16

The simplex method includes which of the following components?

Accepted Answer

A) An initialization step. 
B) An optimality test. 
C) A trial step. 
D) All of the above. 
E) a and b only. 
A) An initialization step. 
B) An optimality test. 
C) A trial step. 
D) All of the above. 
E) a and b only.

Question 17

The data given in the Solver's sensitivity report are obtained directly from the output of the simplex method.

Accepted Answer

A) True 
 B)False

Question 18

The optimal solution of a feasible linear programming problem includes at least one corner point of the feasible region.

Accepted Answer

A) True 
 B)False

Question 19

Management science algorithms are typically iterative algorithms.

Accepted Answer

A) True 
 B)False

Question 20

Most linear programming problems have just one optimal solution.

Accepted Answer

A) True 
 B)False

Exam 17: CD - Solution Concepts for Linear Programming

The computer time per iteration for an interior-point algorithm is approximately the same as for the simplex method.

If some necessary constraints were not included in a linear programming model,it is possible to have no limit on the best objective function value.

It is possible in some cases for a point inside the boundary of the feasible region to be an optimal solution.

It is possible to have more than one corner point of the feasible region as an optimal solution to a linear programming problem.

The simplex method is a specific type of interior-point algorithm.

When choosing which corner point to advance to next,the simplex method chooses the one:

Interior-point algorithms have limited capability for what-if analysis. Multiple Choice Questions

If the feasible region in a linear programming problem is unbounded,then the problem is unbounded.

There are no feasible solutions to a problem when the constraints are too restrictive.

The simplex method can only solve problems with up to 100 functional constraints.

The simplex method is which kind of algorithm?

In a linear programming problem,there is always at least one optimal corner point.

The enumeration-of-corner-points method is limited because it can only solve problems with two decision variables.

Whenever possible,the initialization step of the simplex method chooses the origin to be the initial corner point.

If a linear programming problem does not have an optimal solution then it must be infeasible.

The simplex method includes which of the following components?

The data given in the Solver's sensitivity report are obtained directly from the output of the simplex method.

The optimal solution of a feasible linear programming problem includes at least one corner point of the feasible region.

Management science algorithms are typically iterative algorithms.

Most linear programming problems have just one optimal solution.

Introduction

Linear Programming: Basic Concepts

Linear Programming: Formulation and Applications

The Art of Modeling With Spreadsheets

What-If Analysis for Linear Programming

Network Optimization Problems

Using Binary Integer Programming to Deal With Yes-Or-No Decisions

Nonlinear Programming

Decision Analysis

Cd Supplement - Decision Analysis

Forecasting

Queueing Models

CD Supplement - Additional Queueing Models

Computer Simulation: Basic Concepts

CD Supplement - the Inverse Transformation Method for Generating Random Observations

Computer Simulation With Crystal Ball

CD - Transportation and Assignment Problems

CD - Pertcpm Models for Project Management

CD - Goal Programming

CD - Inventory Management With Known Demand

CD - Inventory Management With Uncertain Demand

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