Backtracking & DP Knapsack Quick Quiz

Oui ou Non

Parties jouées 2

Créer une copie

À propos de cette activité

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

Créé par

Siddhi Kakad

United States

Téléchargez la version pour jouer sur papier

Top 10 résultats

Il n'y a toujours pas de résultats pour ce jeu. Soyez le premier à apparaître dans le classement! Connectez-vous pour vous identifier.

Créez votre propre jeu gratuite à partir de notre créateur de jeu

Affrontez vos amis pour voir qui obtient le meilleur score dans ce jeu

Créer un défi


Backtracking & DP Knapsack Quick QuizVersion en ligne Test your understanding of backtracking, DP, and knapsack concepts. par 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 QuizVersion en ligne

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

par 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

Vous pouvez intégrer le jeu dans un LMS compatible avec LTI 1.1 ou LTI 1.3 comme Canvas, Moodle ou Blackboard. Les scores seront ainsi automatiquement enregistrés dans le carnet de notes de la plateforme.

Backtracking & DP Knapsack Quick Quiz

Oui ou Non

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

Top Jeux

Oui ou Non

Adjective Checker

Oui ou Non

Foot and Ankle Anatomy Quiz

Oui ou Non

Is it true?

Oui ou Non

Grammys for best song/record

Oui ou Non

Is it created with Artificial Intelligence? - Complete game

Backtracking & DP Knapsack Quick QuizVersion en ligne

par 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.

		%
	Anonyme	%
		%
		%