0 1 Knapsack Problem in DSA Javascript - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the '0 1' in 0 1 Knapsack Problem mean?

It means each item can either be taken whole (1) or left (0). You cannot take fractions of an item.

beginner

What is the main goal of the 0 1 Knapsack Problem?

To maximize the total value of items put in the knapsack without exceeding its weight capacity.

intermediate

Which technique is commonly used to solve the 0 1 Knapsack Problem efficiently?

Dynamic Programming is used to solve it efficiently by building solutions to smaller subproblems.

intermediate

In the dynamic programming solution, what does the table represent?

The table stores the maximum value achievable with the first i items and a knapsack capacity j.

intermediate

Why can't greedy algorithms solve the 0 1 Knapsack Problem optimally?

Because taking the item with the best value or best value-to-weight ratio first may not lead to the best overall solution.

In the 0 1 Knapsack Problem, what does the '1' signify?

A You can take only one unit of each item

B You can take fractions of items

C You can take unlimited quantities of items

D You must take all items

The '1' means each item can be taken at most once, so only one unit of each item is allowed.

Which method is best to solve the 0 1 Knapsack Problem for large inputs?

A Brute force checking all subsets

B Dynamic Programming

C Greedy algorithm

D Sorting items by weight

Dynamic Programming efficiently solves the problem by storing results of subproblems.

What does the DP table cell dp[i][w] represent?

A Maximum value using first i items with capacity w

B Weight of the ith item

C Number of items taken

D Minimum weight to reach value w

dp[i][w] stores the best value achievable with first i items and capacity w.

If an item’s weight is more than the current capacity, what happens in DP?

A Item is included anyway

B Value is doubled

C Capacity is increased

D Item is skipped

If the item is too heavy, it cannot be included, so we skip it.

Why is the 0 1 Knapsack Problem not solved optimally by greedy approach?

A Because it only works for fractional knapsack

B Because it requires sorting items

C Because greedy ignores combinations of items

D Because it uses recursion

Greedy approach fails because it does not consider all combinations, which are needed for optimal 0 1 knapsack.

Explain how dynamic programming solves the 0 1 Knapsack Problem step-by-step.

Think about how you choose items one by one and remember best values for smaller capacities.

Describe why the greedy method works for fractional knapsack but not for 0 1 knapsack.

Consider what happens if you cannot break items into parts.

Practice

1. What does the 0 1 Knapsack Problem aim to maximize given a set of items with weights and values?

easy

A. The total value of items without exceeding the weight limit

B. The total weight of items without exceeding the value limit

C. The number of items selected regardless of weight

D. The minimum weight of items selected

Solution

1. Step 1: Understand the problem goal

   The 0 1 Knapsack Problem tries to select items to maximize total value without exceeding the weight capacity.

2. Step 2: Identify the constraint

   The total weight of selected items must not exceed the knapsack's weight limit.

3. Final Answer:

   The total value of items without exceeding the weight limit -> Option A

4. Quick Check:

   Maximize value with weight limit = B [OK]

Hint: Remember: maximize value, not weight or count [OK]

Common Mistakes:

• Confusing weight limit with value limit
• Thinking the problem maximizes number of items
• Ignoring the weight constraint

2. Which of the following is the correct way to initialize the DP array for the 0 1 Knapsack Problem in JavaScript?

easy

A. const dp = new Array(n + 1).fill(new Array(W + 1).fill(0));

B. const dp = Array(n + 1).fill(Array(W + 1).fill(0));

C. const dp = Array.from({ length: n + 1 }, () => Array(W + 1).fill(0));

D. const dp = Array(n).fill(Array(W).fill(0));

Solution

1. Step 1: Understand DP array initialization

   We need a 2D array where each row is a separate array to avoid reference issues.

2. Step 2: Choose correct initialization

   Using Array.from with a function creates independent inner arrays, preventing shared references.

3. Final Answer:

   const dp = Array.from({ length: n + 1 }, () => Array(W + 1).fill(0)); -> Option C

4. Quick Check:

   Independent inner arrays = A [OK]

Hint: Use Array.from with function to avoid shared references [OK]

Common Mistakes:

• Using fill with array directly causing shared references
• Initializing with wrong dimensions
• Forgetting to fill inner arrays

3. What is the output of the following code snippet for weights = [1, 3, 4], values = [15, 50, 60], and W = 5?

function knapsack(weights, values, W) {
  const n = weights.length;
  const dp = Array.from({ length: n + 1 }, () => Array(W + 1).fill(0));
  for (let i = 1; i <= n; i++) {
    for (let w = 1; w <= W; w++) {
      if (weights[i - 1] <= w) {
        dp[i][w] = Math.max(dp[i - 1][w], dp[i - 1][w - weights[i - 1]] + values[i - 1]);
      } else {
        dp[i][w] = dp[i - 1][w];
      }
    }
  }
  return dp[n][W];
}

console.log(knapsack([1, 3, 4], [15, 50, 60], 5));

medium

A. 65

B. 80

C. 60

D. 75

Solution

1. Step 1: Trace DP table filling

   Check items one by one and weights up to 5:
   - Item 1 (weight 1, value 15): dp[1][1..5] = 15
   - Item 2 (weight 3, value 50): dp[2][3] = max(15, 0+50)=50, dp[2][4]=max(15,15+50)=65, dp[2][5]=max(15,15+50)=65
   - Item 3 (weight 4, value 60): dp[3][4]=max(65,0+60)=65, dp[3][5]=max(65,15+60)=75

2. Step 2: Find final answer

   The maximum value for weight 5 is dp[3][5] = 75.

3. Final Answer:

   75 -> Option D

4. Quick Check:

   Max value at W=5 = 75 [OK]

Hint: Trace DP table row by row for each item [OK]

Common Mistakes:

• Adding values without checking weight limit
• Confusing indices in dp array
• Forgetting to compare including vs excluding item

4. Identify the bug in this 0 1 Knapsack implementation and select the fix:

function knapsack(weights, values, W) {
  const n = weights.length;
  const dp = Array.from({ length: n + 1 }, () => Array(W + 1).fill(0));
  for (let i = 1; i <= n; i++) {
    for (let w = 1; w <= W; w++) {
      if (weights[i] <= w) {
        dp[i][w] = Math.max(dp[i - 1][w], dp[i - 1][w - weights[i]] + values[i]);
      } else {
        dp[i][w] = dp[i - 1][w];
      }
    }
  }
  return dp[n][W];
}

medium

A. Change weights[i] and values[i] to weights[i - 1] and values[i - 1]

B. Change loop to start i from 0 instead of 1

C. Initialize dp with zeros using fill(1)

D. Change inner loop to run from w = 0 to W - 1

Solution

1. Step 1: Identify index error in array access

   Arrays in JavaScript are zero-indexed, but loop starts at 1, so weights[i] accesses out-of-bound or wrong element.

2. Step 2: Correct index usage

   Use weights[i - 1] and values[i - 1] to access correct item corresponding to dp row i.

3. Final Answer:

   Change weights[i] and values[i] to weights[i - 1] and values[i - 1] -> Option A

4. Quick Check:

   Fix index offset = D [OK]

Hint: Remember dp row i corresponds to item i-1 [OK]

Common Mistakes:

• Using wrong indices causing undefined values
• Changing loop start incorrectly
• Incorrect dp initialization

5. Given items with weights = [2, 3, 5, 7], values = [10, 5, 15, 7], and knapsack capacity W = 10, which set of items maximizes value without exceeding weight? Select the correct maximum value.
Note: You can only take each item once.

hard

A. 25

B. 30

C. 27

D. 22

Solution

1. Step 1: Consider all item combinations within weight limit 10

   Possible combinations:
   - Items 1,2,3: weights 2+3+5=10, values 10+5+15=30 (exceeds capacity? No, equals 10)
   - Items 1,3,4: weights 2+5+7=14 (exceeds 10)
   - Items 2,3,4: weights 3+5+7=15 (exceeds 10)
   - Items 1,4: weights 2+7=9, values 10+7=17
   - Items 3 alone: weight 5, value 15
   - Items 1,2,4: weights 2+3+7=12 (exceeds 10)

2. Step 2: Find max value without exceeding weight

   Best valid combination is items 1,2,3 with total weight 10 and value 30. But since weight is exactly 10, it's valid.
   Check if any other combination gives more value:
   Items 1,3: weight 7, value 25
   Items 3,4: weight 12 (exceeds)
   So max value is 30.

3. Final Answer:

   30 -> Option B

4. Quick Check:

   Max value with W=10 = 30 [OK]

Hint: Try all combos within weight limit to find max value [OK]

Common Mistakes:

• Ignoring exact weight limit equality
• Adding values of items exceeding capacity
• Missing best combination