Backtracking & DP Knapsack Quick Quiz

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About this activity

Test your understanding of backtracking, DP, and knapsack concepts.

Created by

Siddhi Kakad

United States

Download the paper version to play

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Backtracking & DP Knapsack Quick QuizOnline version Test your understanding of backtracking, DP, and knapsack concepts. by Siddhi Kakad 1 Pruning in Branch and Bound relies only on feasibility checks and never uses any bounds. Yes No 2 A 0/1 Knapsack can be solved with Dynamic Programming using a table of size n by M. Yes No 3 Branch and Bound uses a bounding function to prune non-promising branches. Yes No 4 Backtracking guarantees finding a valid solution if one exists by exploring feasible branches. Yes No 5 A state-space tree represents decisions at each level, typically for inclusion or exclusion of items. Yes No 6 Dynamic Programming is always faster than Branch and Bound for every problem type. Yes No 7 For the provided knapsack example with profits (3,5,6,10) and weights (2,3,4,5) and capacity 8, the optimal solution is items 2 and 4 with total profit 15. Yes No 8 The knapsack problem with fractional items uses the same Dynamic Programming approach as the 0/1 version. Yes No 9 In the given knapsack example, the maximum profit is 13. Yes No 10 In a state-space tree, leaves can never correspond to dead ends or infeasible partial solutions. Yes No

Backtracking & DP Knapsack Quick QuizOnline version

Test your understanding of backtracking, DP, and knapsack concepts.

by Siddhi Kakad

Pruning in Branch and Bound relies only on feasibility checks and never uses any bounds.

Yes

A 0/1 Knapsack can be solved with Dynamic Programming using a table of size n by M.

Yes

Branch and Bound uses a bounding function to prune non-promising branches.

Yes

Backtracking guarantees finding a valid solution if one exists by exploring feasible branches.

Yes

A state-space tree represents decisions at each level, typically for inclusion or exclusion of items.

Yes

Dynamic Programming is always faster than Branch and Bound for every problem type.

Yes

For the provided knapsack example with profits (3,5,6,10) and weights (2,3,4,5) and capacity 8, the optimal solution is items 2 and 4 with total profit 15.

Yes

The knapsack problem with fractional items uses the same Dynamic Programming approach as the 0/1 version.

Yes

In the given knapsack example, the maximum profit is 13.

Yes

In a state-space tree, leaves can never correspond to dead ends or infeasible partial solutions.

Yes

You can integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

Backtracking & DP Knapsack Quick Quiz

Yes or No

About this activity

Created by

Download the paper version to play

Top Games

Yes or No

Is it true?

Yes or No

Foot and Ankle Anatomy Quiz

Yes or No

Grammys for best song/record

Yes or No

Adjective Checker

Yes or No

Is it created with Artificial Intelligence? - Complete game

Backtracking & DP Knapsack Quick QuizOnline version

by Siddhi Kakad

Pruning in Branch and Bound relies only on feasibility checks and never uses any bounds.

A 0/1 Knapsack can be solved with Dynamic Programming using a table of size n by M.

Branch and Bound uses a bounding function to prune non-promising branches.

Backtracking guarantees finding a valid solution if one exists by exploring feasible branches.

A state-space tree represents decisions at each level, typically for inclusion or exclusion of items.

Dynamic Programming is always faster than Branch and Bound for every problem type.

For the provided knapsack example with profits (3,5,6,10) and weights (2,3,4,5) and capacity 8, the optimal solution is items 2 and 4 with total profit 15.

The knapsack problem with fractional items uses the same Dynamic Programming approach as the 0/1 version.

In the given knapsack example, the maximum profit is 13.

In a state-space tree, leaves can never correspond to dead ends or infeasible partial solutions.

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