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WorksheetsPTT361 Quiz 7: Lecture 13
Total questions: 10
Worksheet time: 4mins
Which of the following was discussed in the Lecture 13 video?
Quasi-Newton's Method
Linear Programming
Direct Substitution Method
Simplex Algorithm
When should we use non-linear programming (NLP) in process optimization?
when the objective function and the constraints are in linear form
when the objective function or the constraints are in linear form
when both of the objective function and the constraints are in non-linear form
when either of the objective function or the constraints is in non-linear form
Which of the following is a method used in non-linear programming (NLP)?
Direct substitution
Newton's method
Quasi-Newton's method
Non-simplex Algorithm
Which of the following best describes the direct substitution method in non-linear programming (NLP)?
There are two steps involved in the direct substitution method in NLP
The steps involved in minimizing and maximizing the NLP are different from each other
If there are two variables in one objective function, either one of the variables must be solved first before the next can be solved.
The iteration can be stopped when the difference between the variable values is below the 2% confident level
Which of the following is true about the direct substitution method in NLP?
The direct substitution method is a method of handling LP to solve the problem explicitly for one variable and eliminate the other variable from the problem formulation
The three steps involved in direct substitution in NLP involves choosing the variable to be eliminated
The direct substitution method in NLP can be best described as forcing the 2D graph into a 3D graph
The contour of the objective function can be identified when the 2D graph is lotted
How do we find the nature of the optimum point using the direct substitution method in NLP?
Calculate the second derivative of the objective function and check the sign of the value after substituting the respective variable values
Calculate the first derivative of the objective function and check the sign of the value after substituting the respective variable values
Calculate the third derivative of the objective function and check the sign of the value after substituting the respective variable values
Calculate the value of the objective function and check the sign of the value after substituting the respective variable values
The first-order necessary conditions for a local extremum is also known as:
Lagrange method
Newton's method
Quasi-Newton's method
Quasi-Lagrange method
Which of the following is correct for the first-order necessary conditions for a local extremum method in NLP?
It was proposed by a mathematician named Johnson Louis Lagrange
At an optimum point,
h=0At an optimum point, both objective function and the constraint function are colinear to each other
The Lagrange multiplier is usually calculated before the calculation of the other variables in both of the objective function and the constraints.
The maximum value of the objective function in Example 8.9 given on the last page of the lecture note is:
105
150
1050
1510
The minimum value of the objective function in Example 8.10 given on the last page of the lecture note is:
70
700
7000
70000
