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Assignment 1: Greedy and Dynamic Programming

Authored by Geetha M

Professional Development

Professional Development

Used 6+ times

Assignment 1:    Greedy and Dynamic Programming
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30 questions

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1.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

Which of the following problems is NOT solved using dynamic programming?

0/1 knapsack problem

Matrix chain multiplication problem

Edit distance problem

Fractional knapsack problem

2.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

A greedy algorithm can be used to solve all the dynamic programming problems.

True

False

3.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

Dynamic programming differs from the greedy method since the greedy method produces only one feasible solution, which may or may not be optimal, while dynamic programming produces all possible sub-problems at most once, one of which guaranteed to be optimal.

True

False

4.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

In dynamic programming, the technique of storing the previously calculated values is called ___________

Saving value property

Storing value property

Memorization

Mapping

5.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

What are 2 things required in order to successfully use the dynamic programming technique?

Optimal sub structure and overlapping sub problems

A problem that can’t be sub divided and is complex

Non overlapping sub problems and intervals

Recursion and a problem that is complex

6.

MULTIPLE SELECT QUESTION

10 mins • 1 pt

Dynamic Programming is often used for (choose all that apply):

Optimization problems that involve making a choice that leave one or more subproblems to be solved.

Problems previously solved using divide and conquer that have over lapping subproblems

Non polynomial solution problems

Subproblems where resources are shared

7.

MULTIPLE CHOICE QUESTION

10 mins • 1 pt

The difference between Divide and Conquer and Dynamic Programming is:

The division of problems and combination of subproblems

Whether the sub problems overlap or not

The way we solve the base case

The depth of recurrence

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