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DSA C++programming~10 mins

Greedy vs DP How to Know Which to Apply in DSA C++ - Interactive Comparison Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to check if a problem can be solved greedily by comparing choices.

DSA C++
bool canUseGreedy(const vector<int>& choices) {
    for (int i = 1; i < choices.size(); ++i) {
        if (choices[i] < choices[i-1]) {
            return [1];
        }
    }
    return true;
}
Drag options to blanks, or click blank then click option'
Afalse
Btrue
Cchoices[i]
Dchoices[i-1]
Attempts:
3 left
💡 Hint
Common Mistakes
Returning true instead of false when a smaller choice is found.
2fill in blank
medium

Complete the code to initialize the DP array for storing subproblem results.

DSA C++
vector<int> dp(n + 1, [1]);
Drag options to blanks, or click blank then click option'
AINT_MAX
B0
C-1
Dn
Attempts:
3 left
💡 Hint
Common Mistakes
Using 0 which can be a valid computed value.
3fill in blank
hard

Fix the error in the recursive DP function by filling the correct base case return value.

DSA C++
int solve(int i, const vector<int>& arr) {
    if (i == arr.size()) {
        return [1];
    }
    int take = arr[i] + solve(i + 1, arr);
    int skip = solve(i + 1, arr);
    return max(take, skip);
}
Drag options to blanks, or click blank then click option'
Aarr[i]
B0
C-1
DINT_MIN
Attempts:
3 left
💡 Hint
Common Mistakes
Returning -1 or INT_MIN which breaks max calculation.
4fill in blank
hard

Fill both blanks to complete the greedy algorithm that picks the earliest finishing interval.

DSA C++
sort(intervals.begin(), intervals.end(), [](auto& a, auto& b) {
    return a.[1] < b.[2];
});
Drag options to blanks, or click blank then click option'
Aend
Bstart
Cduration
Dindex
Attempts:
3 left
💡 Hint
Common Mistakes
Sorting by start time instead of end time.
5fill in blank
hard

Fill all three blanks to complete the DP recurrence for the knapsack problem.

DSA C++
dp[i][w] = max(dp[i-1][w], dp[i-1][w-[1]] + [2]);
// where [3] is the weight of item i
Drag options to blanks, or click blank then click option'
Aweights[i]
Bvalues[i]
Attempts:
3 left
💡 Hint
Common Mistakes
Mixing up weights and values in the formula.

Practice

(1/5)
1. Which of the following best describes when to use a greedy algorithm instead of dynamic programming?
easy
A. When the problem has no clear optimal substructure
B. When the problem has optimal substructure and no overlapping subproblems
C. When the problem requires checking all possible solutions
D. When the problem has overlapping subproblems and optimal substructure

Solution

  1. Step 1: Understand greedy algorithm conditions

    Greedy algorithms work well when the problem has an optimal substructure and no overlapping subproblems, meaning making the best local choice leads to a global optimum.
  2. Step 2: Compare with dynamic programming conditions

    Dynamic programming is used when there are overlapping subproblems and optimal substructure, requiring storing intermediate results to avoid recomputation.
  3. Final Answer:

    When the problem has optimal substructure and no overlapping subproblems -> Option B
  4. Quick Check:

    Greedy = optimal substructure + no overlap [OK]
Hint: Use greedy if no overlapping subproblems exist [OK]
Common Mistakes:
  • Confusing overlapping subproblems as a greedy condition
  • Using DP when greedy suffices
  • Ignoring optimal substructure requirement
2. Which of the following C++ code snippets correctly initializes a DP array for a problem with 5 states?
easy
A. int dp[5];
B. int dp(5, 0);
C. vector<int> dp(5, 0);
D. int dp = new int[5];

Solution

  1. Step 1: Check syntax for array initialization

    int dp[5]; leaves elements uninitialized. int dp(5, 0); is invalid syntax for array initialization. int dp = new int[5]; uses incorrect syntax and memory management in C++.
  2. Step 2: Identify correct vector initialization

    vector<int> dp(5, 0); correctly creates a vector of size 5 with all elements initialized to 0, which is the preferred way to initialize DP arrays in modern C++.
  3. Final Answer:

    vector<int> dp(5, 0); -> Option C
  4. Quick Check:

    Use vector<int> dp(size, 0) for DP arrays [OK]
Hint: Use vector with size and initial value for DP arrays [OK]
Common Mistakes:
  • Using incorrect syntax for array initialization
  • Not initializing all elements to zero
  • Using raw pointers without delete
3. Consider the following C++ code snippet for a problem that can be solved by either greedy or DP. What is the output?
int main() {
    int arr[] = {1, 2, 3, 4, 5};
    int n = 5;
    int sum = 0;
    for (int i = 0; i < n; i++) {
        if (arr[i] % 2 == 0) {
            sum += arr[i];
        }
    }
    std::cout << sum << std::endl;
    return 0;
}
medium
A. 6
B. 9
C. 15
D. 10

Solution

  1. Step 1: Identify even numbers in the array

    The array is {1, 2, 3, 4, 5}. Even numbers are 2 and 4.
  2. Step 2: Calculate sum of even numbers

    Sum = 2 + 4 = 6.
  3. Final Answer:

    6 -> Option A
  4. Quick Check:

    Sum of even numbers = 6 [OK]
Hint: Sum only even numbers using modulo check [OK]
Common Mistakes:
  • Summing all numbers instead of only even
  • Off-by-one error in loop
  • Misunderstanding modulo operator
4. The following code attempts to solve the coin change problem using a greedy approach. What is the main issue?
int coinChange(int amount) {
    int coins[] = {25, 10, 5, 1};
    int count = 0;
    for (int i = 0; i < 4; i++) {
        while (amount >= coins[i]) {
            amount -= coins[i];
            count++;
        }
    }
    return count;
}
medium
A. Greedy approach fails for some coin sets, not always optimal
B. The loop condition is incorrect and causes infinite loop
C. Coins array is not sorted in ascending order
D. The function does not return the count

Solution

  1. Step 1: Understand greedy approach for coin change

    The greedy approach picks the largest coin repeatedly. It works only if coin denominations are canonical (like US coins).
  2. Step 2: Identify problem with greedy for arbitrary coins

    For some coin sets, greedy does not give minimum coins. Dynamic programming is needed for guaranteed optimality.
  3. Final Answer:

    Greedy approach fails for some coin sets, not always optimal -> Option A
  4. Quick Check:

    Greedy coin change not always optimal [OK]
Hint: Greedy coin change fails if coins not canonical [OK]
Common Mistakes:
  • Assuming greedy always gives minimum coins
  • Ignoring coin set properties
  • Confusing loop correctness with algorithm correctness
5. You have a set of tasks each with a deadline and profit. You want to schedule tasks to maximize profit without missing deadlines. Which approach is best and why?
struct Task {
    int deadline;
    int profit;
};
hard
A. Use greedy algorithm by sorting tasks by deadline only
B. Use dynamic programming to try all subsets of tasks and pick max profit
C. Use brute force to check all permutations of tasks
D. Use greedy algorithm by sorting tasks by profit and scheduling latest available slot

Solution

  1. Step 1: Understand task scheduling problem

    This is a classic greedy problem where tasks are sorted by profit descending, then scheduled in the latest free slot before deadline.
  2. Step 2: Compare approaches

    Dynamic programming or brute force is too slow. Sorting by deadline only does not maximize profit. Greedy by profit with latest slot is efficient and optimal.
  3. Final Answer:

    Use greedy algorithm by sorting tasks by profit and scheduling latest available slot -> Option D
  4. Quick Check:

    Greedy by profit + latest slot = max profit [OK]
Hint: Sort tasks by profit, schedule latest free slot [OK]
Common Mistakes:
  • Sorting only by deadline misses max profit
  • Trying DP or brute force unnecessarily
  • Scheduling tasks without checking deadlines
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