Fractional Knapsack Problem in DSA C - Time & Space Complexity

Time Complexity: Fractional Knapsack Problem

O(n log n)

Understanding Time Complexity

We want to understand how the time needed to solve the Fractional Knapsack Problem changes as the number of items grows.

Specifically, how does the algorithm's work increase when we have more items to consider?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Fractional Knapsack Problem
// items[] contains pairs of (value, weight)
// n is number of items, W is max capacity

// Step 1: Calculate value per weight for each item
// Step 2: Sort items by value per weight descending
// Step 3: Take items fully or partially until capacity fills

void fractionalKnapsack(int n, int W, Item items[]) {
    // Sort items by value/weight ratio
    qsort(items, n, sizeof(Item), compare);

    int currentWeight = 0;
    double totalValue = 0.0;
    for (int i = 0; i < n; i++) {
        if (currentWeight + items[i].weight <= W) {
            currentWeight += items[i].weight;
            totalValue += items[i].value;
        } else {
            int remain = W - currentWeight;
            totalValue += items[i].value * ((double) remain / items[i].weight);
            break;
        }
    }
}
    

This code sorts items by their value per weight and then picks as much as possible from the best items until the knapsack is full.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Sorting the array of items by value per weight.
• How many times: Sorting takes about n log n steps for n items.
• Secondary operation: One loop through all items to pick them.
• How many times: This loop runs at most n times.

How Execution Grows With Input

As the number of items increases, the sorting step grows faster than the picking loop.

Input Size (n)	Approx. Operations
10	About 10 * log 10 ≈ 33 operations for sorting + 10 for picking
100	About 100 * log 100 ≈ 664 operations for sorting + 100 for picking
1000	About 1000 * log 1000 ≈ 9966 operations for sorting + 1000 for picking

Pattern observation: The sorting step dominates and grows faster than the picking loop as n increases.

Final Time Complexity

Time Complexity: O(n log n)

This means the time needed grows a bit faster than just the number of items because sorting takes extra steps as the list grows.

Common Mistake

[X] Wrong: "The picking loop is the slowest part, so the time complexity is O(n)."

[OK] Correct: Sorting the items takes more time than just picking them, so the overall time depends mostly on sorting, which is O(n log n).

Interview Connect

Understanding how sorting affects the time helps you explain why some steps take longer and shows you can analyze algorithms beyond just loops.

Self-Check

"What if the items were already sorted by value per weight? How would the time complexity change?"

Practice

1. What is the main goal of the Fractional Knapsack Problem?

easy

A. Select items only if their weight is less than a fixed number

B. Minimize total weight of items selected

C. Maximize total value by taking fractions of items within weight limit

D. Find the number of items that fit exactly in the knapsack

Solution

1. Step 1: Understand the problem goal

   The fractional knapsack problem allows taking parts of items to maximize total value within a weight limit.

2. Step 2: Compare options

   Only Maximize total value by taking fractions of items within weight limit correctly describes maximizing value by taking fractions within weight limit.

3. Final Answer:

   Maximize total value by taking fractions of items within weight limit -> Option C

4. Quick Check:

   Goal = Maximize value with fractions [OK]

Hint: Remember: fractional means partial items allowed [OK]

Common Mistakes:

• Thinking only whole items can be taken
• Confusing with minimizing weight
• Ignoring value maximization

2. Which of the following is the correct way to sort items by value-to-weight ratio in C for the fractional knapsack?

easy

A. qsort(items, n, sizeof(Item), compare); where compare sorts by value/weight descending

B. sort(items, items+n); without a comparator

C. qsort(items, n, sizeof(Item), compare); where compare sorts by weight ascending

D. Using bubble sort on values only

Solution

1. Step 1: Identify sorting requirement

   Items must be sorted by value-to-weight ratio descending for greedy selection.

2. Step 2: Match sorting method

   Using qsort with a comparator sorting by value/weight descending is correct.

3. Final Answer:

   qsort(items, n, sizeof(Item), compare); where compare sorts by value/weight descending -> Option A

4. Quick Check:

   Sort by value/weight ratio descending [OK]

Hint: Sort by value/weight ratio descending before picking [OK]

Common Mistakes:

• Sorting by weight only
• Sorting without comparator
• Using inefficient sorting for large data

3. What is the output of the following C code snippet for fractional knapsack?

typedef struct { int value; int weight; } Item;

Item items[] = {{60, 10}, {100, 20}, {120, 30}};
int n = 3, W = 50;

// Assume items sorted by value/weight descending
float fractionalKnapsack(Item items[], int n, int W) {
    int curWeight = 0;
    float finalValue = 0.0;
    for (int i = 0; i < n; i++) {
        if (curWeight + items[i].weight <= W) {
            curWeight += items[i].weight;
            finalValue += items[i].value;
        } else {
            int remain = W - curWeight;
            finalValue += items[i].value * ((float) remain / items[i].weight);
            break;
        }
    }
    return finalValue;
}

printf("%.2f", fractionalKnapsack(items, n, W));

medium

A. 240.00

B. 220.00

C. 180.00

D. 200.00

Solution

1. Step 1: Calculate value/weight ratios and sort

   Ratios: 60/10=6, 100/20=5, 120/30=4. Sorted order is as given.

2. Step 2: Add items until weight limit 50

   Add first item fully: weight=10, value=60; second fully: weight=30, value=160; third partially: remain=20, value=120*(20/30)=80; total value=60+100+80=240.

3. Final Answer:

   240.00 -> Option A

4. Quick Check:

   Sum values with fractions = 240.00 [OK]

Hint: Add full items then fraction of next to fill knapsack [OK]

Common Mistakes:

• Not sorting by ratio
• Adding partial value incorrectly
• Ignoring remaining capacity

4. Find the error in this C code snippet for fractional knapsack:

float fractionalKnapsack(Item items[], int n, int W) {
    int curWeight = 0;
    float finalValue = 0.0;
    for (int i = 0; i < n; i++) {
        if (curWeight + items[i].weight < W) {
            curWeight += items[i].weight;
            finalValue += items[i].value;
        } else {
            int remain = W - curWeight;
            finalValue += items[i].value * ((float) remain / items[i].weight);
            break;
        }
    }
    return finalValue;
}

medium

A. The remain calculation is incorrect

B. The finalValue should be int, not float

C. The loop should run from i=1 to n

D. The condition should be <= instead of < to include full weight equal to W

Solution

1. Step 1: Analyze the weight check condition

   The condition curWeight + items[i].weight < W excludes items that exactly fill the knapsack.

2. Step 2: Correct the condition

   Changing to <= allows taking items that fit exactly, avoiding missing full items.

3. Final Answer:

   The condition should be <= instead of < to include full weight equal to W -> Option D

4. Quick Check:

   Use <= to include exact weight [OK]

Hint: Use <= to allow full item if it fits exactly [OK]

Common Mistakes:

• Using < excludes exact fit items
• Changing variable types unnecessarily
• Incorrect loop indices

5. Given items with values and weights: {value: 40, weight: 2}, {value: 100, weight: 20}, {value: 50, weight: 10}, and a knapsack capacity of 26, what is the maximum value achievable using fractional knapsack?

hard

A. 190

B. 160

C. 180

D. 140

Solution

1. Step 1: Calculate value/weight ratios and sort items

   Ratios: 40/2=20, 100/20=5, 50/10=5. Sorted descending: {40,2}, {100,20}, {50,10}.

2. Step 2: Greedy selection with capacity 26

   Take {40,2} full: w=2, v=40, remain=24.
   Take {100,20} full: w=22, v=140, remain=4.
   Fraction of {50,10}: (4/10)*50=20, total v=160.

3. Final Answer:

   160 -> Option B

4. Quick Check:

   Max value by greedy fractions = 160 [OK]

Hint: Sort by ratio, take full items, then fraction of next [OK]

Common Mistakes:

• Ignoring sorting by ratio
• Taking items in wrong order
• Miscomputing fractional value