feat: Add Weighted Interval Scheduling

Author: Soumyadipta-Banerjee

dynamic_programming/weighted_interval_scheduling

@@ -0,0 +1,171 @@
+/**
+ * @file
+ * @brief Implements the [Weighted Interval Scheduling]
+ * (https://www.geeksforgeeks.org/dsa/weighted-job-scheduling/) problem.
+ *
+ * @details
+ * The Weighted Interval Scheduling problem is a classic optimization challenge.
+ * Given a set of intervals, each with a start time, end time, and a profit,
+ * the goal is to find a subset of non-overlapping intervals that maximizes
+ * the total profit. This implementation uses dynamic programming combined
+ * with binary search to achieve an efficient solution.
+ *
+ * Example:
+ * Intervals: [{1, 3, 50}, {4, 6, 70}, {6, 19, 100}, {2, 5, 20}]
+ * Max Profit: 120 (by choosing {1, 3, 50} and {4, 6, 70})
+ *
+ * Constraints & Assumptions:
+ * - Interval start times, end times, and profits are non-negative integers.
+ * - The start time of an interval is always less than its end time.
+ * - An empty set of intervals will result in a profit of 0.
+ * - Intervals are considered non-overlapping if one ends before or at the same time the other begins.
+ *
+ * Time Complexity: O(n log n), dominated by the initial sort of intervals.
+ * Space Complexity: O(n) for the dynamic programming table.
+ *
+ * @author [Soumyadipta-Banerjee](https://github.com/Soumyadipta-Banerjee)
+ */
+
+#include <algorithm>      /// for std::sort, std::max
+#include <cassert>        /// for std::assert
+#include <iostream>       /// for IO operations
+#include <vector>         /// for std::vector
+
+/**
+ * @namespace dynamic_programming
+ * @brief Dynamic Programming algorithms
+ */
+namespace dynamic_programming {
+/**
+ * @namespace weighted_interval_scheduling
+ * @brief Functions for the [Weighted Interval Scheduling]
+ * (https://www.geeksforgeeks.org/dsa/weighted-job-scheduling/) problem.
+ */
+namespace weighted_interval_scheduling {
+/**
+ * @brief Struct to represent an interval (or job).
+ */
+struct Interval {
+    int start, end, profit;
+};
+
+/**
+ * @brief Comparison function to sort intervals based on their end time.
+ * @param a First interval
+ * @param b Second interval
+ * @returns true if interval a's end time is less than b's.
+ */
+bool compareIntervals(const Interval& a, const Interval& b) {
+    return a.end < b.end;
+}
+
+/**
+ * @brief Finds the latest non-overlapping interval using binary search.
+ * This is a helper function for the main DP algorithm.
+ * @param intervals Sorted vector of intervals.
+ * @param i Index of the current interval.
+ * @returns Index of the latest compatible interval, or -1 if none exists.
+ */
+int findLatestNonOverlapping(const std::vector<Interval>& intervals, int i) {
+    int low = 0, high = i - 1;
+    int result = -1;
+
+    // Perform binary search to find the latest compatible interval.
+    while (low <= high) {
+        int mid = low + (high - low) / 2;
+        if (intervals[mid].end <= intervals[i].start) {
+            // This interval is compatible, it could be our answer.
+            // But we look for a later one in the right half.
+            result = mid;
+            low = mid + 1;
+        } else {
+            // This interval overlaps, so we search in the left half.
+            high = mid - 1;
+        }
+    }
+    return result;
+}
+
+/**
+ * @brief Main function to solve the Weighted Interval Scheduling problem.
+ * @param intervals A vector of intervals. It will be sorted in-place.
+ * @returns The maximum achievable profit.
+ */
+int findMaxProfit(std::vector<Interval>& intervals) {
+    if (intervals.empty()) {
+        return 0;
+    }
+
+    // Sort intervals based on their end times. This is a prerequisite.
+    std::sort(intervals.begin(), intervals.end(), compareIntervals);
+
+    int n = intervals.size();
+    // dp[i] will store the maximum profit for the first i+1 intervals.
+    std::vector<int> dp(n);
+    // Base case: The max profit for the first interval is its own profit.
+    dp[0] = intervals[0].profit;
+
+    // Fill the DP table from the second interval onwards.
+    for (int i = 1; i < n; ++i) {
+        // Option 1: Include the current interval.
+        // The profit would be its own profit plus the max profit of compatible intervals.
+        int profit_including_current = intervals[i].profit;
+        int latest_non_overlapping_idx = findLatestNonOverlapping(intervals, i);
+        if (latest_non_overlapping_idx != -1) {
+            profit_including_current += dp[latest_non_overlapping_idx];
+        }
+
+        // Option 2: Exclude the current interval.
+        // The profit would be the same as the max profit up to the previous interval.
+        int profit_excluding_current = dp[i - 1];
+
+        // The max profit at this stage is the best of the two options.
+        dp[i] = std::max(profit_including_current, profit_excluding_current);
+    }
+
+    // The last element in the DP table holds the overall maximum profit.
+    return dp[n - 1];
+}
+
+}  // namespace weighted_interval_scheduling
+}  // namespace dynamic_programming
+
+/**
+ * @brief Test Function to verify the implementation with corrected test cases.
+ * @return void
+ */
+static void test() {
+    // Test Case 1: Standard example from GeeksforGeeks, verified.
+    std::vector<dynamic_programming::weighted_interval_scheduling::Interval> intervals1 = {
+        {3, 10, 20}, {1, 2, 50}, {6, 19, 100}, {2, 100, 200}};
+    assert(dynamic_programming::weighted_interval_scheduling::findMaxProfit(intervals1) == 250);
+
+    // Test Case 2: Your original test case, correct expected profit is 150.
+    std::vector<dynamic_programming::weighted_interval_scheduling::Interval> intervals2 = {
+        {1, 3, 50}, {2, 5, 20}, {6, 19, 100}, {4, 6, 70}};
+    assert(dynamic_programming::weighted_interval_scheduling::findMaxProfit(intervals2) == 220);
+
+    // Test Case 3: Another verified online example.
+    std::vector<dynamic_programming::weighted_interval_scheduling::Interval> intervals3 = {
+        {1, 3, 5}, {2, 5, 6}, {4, 6, 5}, {6, 7, 4}, {5, 8, 11}, {7, 9, 2}};
+    assert(dynamic_programming::weighted_interval_scheduling::findMaxProfit(intervals3) == 17);
+
+    // Test Case 4: Edge case with an empty set of intervals.
+    std::vector<dynamic_programming::weighted_interval_scheduling::Interval> intervals4 = {};
+    assert(dynamic_programming::weighted_interval_scheduling::findMaxProfit(intervals4) == 0);
+
+    // Test Case 5: Edge case with a single interval.
+    std::vector<dynamic_programming::weighted_interval_scheduling::Interval> intervals5 = {{1, 100, 55}};
+    assert(dynamic_programming::weighted_interval_scheduling::findMaxProfit(intervals5) == 55);
+
+    std::cout << "All tests passed successfully!\n";
+}
+
+/**
+ * @brief Main function
+ * @returns 0 on exit
+ */
+int main() {
+    test();
+    return 0;
+}