Backtracking & DP Knapsack Quick Quiz

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

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Siddhi Kakad

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Backtracking & DP Knapsack Quick QuizVersión en línea Test your understanding of backtracking, DP, and knapsack concepts. por 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 QuizVersión en línea

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

por 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

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Backtracking & DP Knapsack Quick Quiz

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Backtracking & DP Knapsack Quick QuizVersión en línea

por 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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