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| 1 | +/************************************************************************* |
| 2 | + * @author: Aryan Sisodiya (@InfinityxR9) (https://github.com/InfinityxR9) |
| 3 | + * @category: Problem |
| 4 | + * @date: 9 October, 2025 |
| 5 | + * @name: Search in a 2D Matrix (Implemented as 2D STL Vector) - I |
| 6 | + * DIFFICULTY: Medium |
| 7 | + * |
| 8 | + * LeetCode Reference: https://leetcode.com/problems/search-a-2d-matrix/ |
| 9 | + * Constrains: |
| 10 | + * * m == matrix.length |
| 11 | + * * n == matrix[i].length |
| 12 | + * * 1 <= m, n <= 100 |
| 13 | + * * -104 <= matrix[i][j], target <= 104 |
| 14 | + * |
| 15 | + * @details |
| 16 | + * You are given a m x n integer matrix with the following two properties: |
| 17 | + * * Each row is sorted in non-decreasing order. |
| 18 | + * * The first integer of each row is greater than the last integer of the previous row. |
| 19 | + * |
| 20 | + * Given an integer target, return true if target is in matrix or false otherwise. |
| 21 | + * |
| 22 | + * You must write a solution in O(log(m * n)) time complexity. |
| 23 | + * |
| 24 | + * @example |
| 25 | + * Input: matrix = [[1,3,5,7],[10,11,16,20],[23,30,34,60]], target = 3 |
| 26 | + * Output: true |
| 27 | + * |
| 28 | + * Input: matrix = [[1,3,5,7],[10,11,16,20],[23,30,34,60]], target = 13 |
| 29 | + * Output: false |
| 30 | + * |
| 31 | + * Approach: Using Binary Search Twice |
| 32 | + * * (1) We use binary search first to find out the target lies in which row |
| 33 | + * (2) We use binary search second on that row to find out whether target lies in matrix or not. |
| 34 | + * |
| 35 | + * * The matrix is sorted in a zig-zag fashion. |
| 36 | + * * Hence, the Binary Search approach is very well applicable. |
| 37 | + * |
| 38 | + * |
| 39 | + * Time Complexity: O(log (m * n)) |
| 40 | + * Space Complexity: O(1) |
| 41 | + * |
| 42 | + * |
| 43 | + */ |
| 44 | + |
| 45 | +// Necessary Header Files import |
| 46 | +#include <iostream> |
| 47 | +#include <vector> |
| 48 | +#include <random> |
| 49 | +#include <algorithm> |
| 50 | + |
| 51 | +using namespace std; |
| 52 | + |
| 53 | +/** |
| 54 | + * Core Algorithm using Binary Search twice to find `target` |
| 55 | + * @param matrix The 2D Matrix (with specified properties), in which the `target` is to be searched |
| 56 | + * @param target The value to be searched in the `matrix` |
| 57 | + * @return Whether the `target` is present in the `matrix` or not |
| 58 | + */ |
| 59 | +bool searchMatrix(vector<vector<int>> &matrix, int target) |
| 60 | +{ |
| 61 | + // Variables Assignment to find the row |
| 62 | + int sRow = 0, eRow = matrix.size() - 1, sMid; |
| 63 | + |
| 64 | + while (sRow <= eRow) |
| 65 | + { |
| 66 | + sMid = sRow + (eRow - sRow) / 2; |
| 67 | + |
| 68 | + if (matrix[sMid][0] <= target && |
| 69 | + matrix[sMid][matrix[sMid].size() - 1] >= target) |
| 70 | + break; |
| 71 | + else if (matrix[sMid][0] < target && |
| 72 | + matrix[sMid][matrix[sMid].size() - 1] < target) |
| 73 | + sRow = sMid + 1; // Search in right half |
| 74 | + else |
| 75 | + eRow = sMid - 1; // Search in left half |
| 76 | + } |
| 77 | + |
| 78 | + // Variables assignment for Binary Search in so found row |
| 79 | + int low = 0, high = matrix[sMid].size() - 1, mid; |
| 80 | + |
| 81 | + while (low <= high) |
| 82 | + { |
| 83 | + mid = low + (high - low) / 2; |
| 84 | + |
| 85 | + if (matrix[sMid][mid] == target) |
| 86 | + return true; // target found! |
| 87 | + else if (matrix[sMid][mid] < target) |
| 88 | + low = mid + 1; |
| 89 | + else |
| 90 | + high = mid - 1; |
| 91 | + } |
| 92 | + |
| 93 | + return false; |
| 94 | +} |
| 95 | + |
| 96 | +/** |
| 97 | + * Running Single Test Case |
| 98 | + * @param input The 2D Matrix with specified property |
| 99 | + * @param target The target value to be searched in the 2D Matrix |
| 100 | + * @param expected The expected output |
| 101 | + * @param testName Test Case brief Description |
| 102 | + */ |
| 103 | +void run_test(vector<vector<int>> input, const int target, const bool expected, const string testName) |
| 104 | +{ |
| 105 | + bool ans = searchMatrix(input, target); |
| 106 | + if (ans == expected) |
| 107 | + { |
| 108 | + cout << "[PASS] " << testName << endl; |
| 109 | + } |
| 110 | + else |
| 111 | + { |
| 112 | + cout << "[FAIL] " << testName << endl; |
| 113 | + cout << " Expected: " << expected; |
| 114 | + cout << "\n Got: " << ans; |
| 115 | + cout << "\n\n"; |
| 116 | + } |
| 117 | +} |
| 118 | + |
| 119 | +/** |
| 120 | + * Utility function to build a strictly-increasing flattened matrix with `rows` x `cols`. |
| 121 | + * @param rows Number of rows |
| 122 | + * @param cols Number of columns |
| 123 | + * @param start The start value `default = 1` |
| 124 | + * @param step The start value `default = 1` |
| 125 | + * @return The `matrix` so builded |
| 126 | + */ |
| 127 | +vector<vector<int>> build_increasing_matrix(int rows, int cols, int start = 1, int step = 1) |
| 128 | +{ |
| 129 | + vector<vector<int>> mat(rows, vector<int>()); |
| 130 | + int val = start; |
| 131 | + for (int r = 0; r < rows; ++r) |
| 132 | + { |
| 133 | + for (int c = 0; c < cols; ++c) |
| 134 | + { |
| 135 | + mat[r].push_back(val); |
| 136 | + val += step; |
| 137 | + } |
| 138 | + } |
| 139 | + return mat; |
| 140 | +} |
| 141 | + |
| 142 | +/** |
| 143 | + * Utility function to Run all the test cases |
| 144 | + */ |
| 145 | +void test_cases() |
| 146 | +{ |
| 147 | + // 1-2: Given examples |
| 148 | + run_test({{1, 3, 5, 7}, {10, 11, 16, 20}, {23, 30, 34, 60}}, 3, true, "Example Case 1: Found (middle row)"); |
| 149 | + run_test({{1, 3, 5, 7}, {10, 11, 16, 20}, {23, 30, 34, 60}}, 13, false, "Example Case 2: Not present"); |
| 150 | + |
| 151 | + // 3-4: Single element |
| 152 | + run_test({{5}}, 5, true, "Single element present"); |
| 153 | + run_test({{5}}, -5, false, "Single element absent"); |
| 154 | + |
| 155 | + // 5-8: Single row |
| 156 | + run_test({{1, 3, 5, 7, 9}}, 7, true, "Single row: present (middle)"); |
| 157 | + run_test({{1, 3, 5, 7, 9}}, 2, false, "Single row: absent (between)"); |
| 158 | + run_test({{1, 3, 5, 7, 9}}, 1, true, "Single row: first element"); |
| 159 | + run_test({{1, 3, 5, 7, 9}}, 9, true, "Single row: last element"); |
| 160 | + |
| 161 | + // 9-10: Single column |
| 162 | + run_test({{1}, {3}, {5}, {7}, {9}}, 5, true, "Single column: middle present"); |
| 163 | + run_test({{1}, {3}, {5}, {7}, {9}}, 4, false, "Single column: absent between rows"); |
| 164 | + |
| 165 | + // 11-13: Multi-row: first, last, between |
| 166 | + run_test({{1, 2, 3}, {10, 11, 12}, {20, 21, 22}}, 1, true, "Multi-row: first element"); |
| 167 | + run_test({{1, 2, 3}, {10, 11, 12}, {20, 21, 22}}, 22, true, "Multi-row: last element"); |
| 168 | + run_test({{1, 2, 3}, {10, 11, 12}, {20, 21, 22}}, 15, false, "Multi-row: value between rows"); |
| 169 | + |
| 170 | + // 14-15: Large gaps |
| 171 | + run_test({{1, 2, 3}, {100, 200, 300}, {1000, 2000, 3000}}, 200, true, "Large gap: present"); |
| 172 | + run_test({{1, 2, 3}, {100, 200, 300}, {1000, 2000, 3000}}, 250, false, "Large gap: absent"); |
| 173 | + |
| 174 | + // 16-17: Negatives |
| 175 | + run_test({{-10, -5, -2}, {0, 2, 4}, {10, 20, 30}}, -5, true, "Negative number present"); |
| 176 | + run_test({{-10, -5, -2}, {0, 2, 4}, {10, 20, 30}}, -6, false, "Negative number absent"); |
| 177 | + |
| 178 | + // 18-19: Mix negatives and positives |
| 179 | + run_test({{-100, -50, -10}, {0, 1, 2}, {10, 20, 30}}, 0, true, "Zero present (row boundary)"); |
| 180 | + run_test({{-100, -50, -10}, {0, 1, 2}, {10, 20, 30}}, 3, false, "Positive absent across rows"); |
| 181 | + |
| 182 | + // 20-21: Outside range |
| 183 | + run_test({{5, 10, 15}, {20, 25, 30}}, 1, false, "Target smaller than min"); |
| 184 | + run_test({{5, 10, 15}, {20, 25, 30}}, 35, false, "Target larger than max"); |
| 185 | + |
| 186 | + // 22-23: 2x2 |
| 187 | + run_test({{1, 2}, {3, 4}}, 3, true, "2x2 present"); |
| 188 | + run_test({{1, 2}, {3, 4}}, 5, false, "2x2 absent"); |
| 189 | + |
| 190 | + // 24-25: 3x1 column |
| 191 | + run_test({{2}, {4}, {6}}, 4, true, "3x1 column present"); |
| 192 | + run_test({{2}, {4}, {6}}, 5, false, "3x1 column absent"); |
| 193 | + |
| 194 | + // 26-27: Random small valid matrices |
| 195 | + run_test({{1, 4, 7}, {10, 14, 18}, {25, 30, 35}}, 30, true, "Random small: present"); |
| 196 | + run_test({{1, 4, 7}, {10, 14, 18}, {25, 30, 35}}, 26, false, "Random small: absent"); |
| 197 | + |
| 198 | + // 28-29: 4x4 |
| 199 | + run_test({{1, 2, 3, 4}, {10, 11, 12, 13}, {20, 21, 22, 23}, {30, 31, 32, 33}}, 22, true, "4x4 present"); |
| 200 | + run_test({{1, 2, 3, 4}, {10, 11, 12, 13}, {20, 21, 22, 23}, {30, 31, 32, 33}}, 19, false, "4x4 absent (gap)"); |
| 201 | + |
| 202 | + // 30-32: Boundary extremes (constraints: -10^4 .. 10^4) |
| 203 | + run_test({{-10000, -9999, -9998}, {0, 1, 2}, {9998, 9999, 10000}}, -10000, true, "Boundary min present"); |
| 204 | + run_test({{-10000, -9999, -9998}, {0, 1, 2}, {9998, 9999, 10000}}, 10000, true, "Boundary max present"); |
| 205 | + run_test({{-10000, -9999, -9998}, {0, 1, 2}, {9998, 9999, 10000}}, 5000, false, "Boundary mid absent"); |
| 206 | + |
| 207 | + // 33-34: Larger random-like |
| 208 | + run_test({{1, 3, 5, 7, 9, 11}, {20, 22, 24, 26, 28, 30}, {40, 42, 44, 46, 48, 50}}, 28, true, "Larger random: present"); |
| 209 | + run_test({{1, 3, 5, 7, 9, 11}, {20, 22, 24, 26, 28, 30}, {40, 42, 44, 46, 48, 50}}, 29, false, "Larger random: absent"); |
| 210 | + |
| 211 | + // 35-36: Sequential 1..9 |
| 212 | + run_test({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, 8, true, "Sequential present"); |
| 213 | + run_test({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}, 10, false, "Sequential absent"); |
| 214 | + |
| 215 | + // 37-38: Rectangular 2x5 |
| 216 | + run_test({{1, 2, 3, 4, 5}, {10, 11, 12, 13, 14}}, 12, true, "Rectangular 2x5 present"); |
| 217 | + run_test({{1, 2, 3, 4, 5}, {10, 11, 12, 13, 14}}, 9, false, "Rectangular 2x5 absent"); |
| 218 | + |
| 219 | + // 39-40: Row transitions |
| 220 | + run_test({{1, 2, 3}, {10, 11, 12}, {20, 21, 22}}, 10, true, "Row transition: first element of middle row"); |
| 221 | + run_test({{1, 2, 3}, {10, 11, 12}, {20, 21, 22}}, 12, true, "Row transition: last element of middle row"); |
| 222 | + |
| 223 | + // 41-42: Mixed intervals |
| 224 | + run_test({{2, 4, 6, 8}, {15, 20, 25, 30}, {100, 200, 300, 400}}, 300, true, "Mixed intervals: present"); |
| 225 | + run_test({{2, 4, 6, 8}, {15, 20, 25, 30}, {100, 200, 300, 400}}, 99, false, "Mixed intervals: absent"); |
| 226 | + |
| 227 | + // 43-44: Repeated values inside rows (allowed) but strict across rows |
| 228 | + run_test({{1, 1, 1, 2}, {3, 3, 3, 4}}, 1, true, "Repeateds in row: find repeat value"); |
| 229 | + run_test({{1, 1, 1, 2}, {3, 3, 3, 4}}, 3, true, "Repeateds in next row: find repeat value"); |
| 230 | + |
| 231 | + // 45: All-equal single row |
| 232 | + run_test({{2, 2, 2, 2}}, 2, true, "Single row all-equal: present"); |
| 233 | + |
| 234 | + // 46: Varying row lengths |
| 235 | + run_test({{1, 2}, {3, 4, 5}, {6}}, 5, true, "Varying row lengths: present"); |
| 236 | + |
| 237 | + // 47: Absent just after first row |
| 238 | + run_test({{1, 2, 3}, {10, 11, 12}}, 4, false, "Absent just after first row (gap)"); |
| 239 | + |
| 240 | + // 48: Negative to zero transition with duplicates |
| 241 | + run_test({{-3, -2, -1}, {0, 0, 1}}, 0, true, "Negative->Zero with duplicates: present"); |
| 242 | + |
| 243 | + // 49: Long single row (n = 100) - generated |
| 244 | + { |
| 245 | + vector<int> longRow; |
| 246 | + longRow.reserve(100); |
| 247 | + for (int i = 0; i < 100; ++i) |
| 248 | + longRow.push_back(i * 2 + 1); // odd numbers 1..199 |
| 249 | + run_test({longRow}, 199, true, "Long single row (n=100): last element present"); |
| 250 | + run_test({longRow}, 100, false, "Long single row (n=100): absent even number"); |
| 251 | + } |
| 252 | + |
| 253 | + // 50-52: Programmatic, deterministic random-ish matrices (fixed seed) |
| 254 | + { |
| 255 | + mt19937 rng(42); |
| 256 | + for (int t = 0; t < 3; ++t) |
| 257 | + { |
| 258 | + int rows = 1 + (rng() % 6); // 1..6 rows |
| 259 | + int cols = 1 + (rng() % 8); // 1..8 cols |
| 260 | + int start = -50 + (rng() % 101); // -50..50 |
| 261 | + int step = 1 + (rng() % 5); // 1..5 |
| 262 | + auto M = build_increasing_matrix(rows, cols, start, step); |
| 263 | + |
| 264 | + // pick a random cell to be the target (should be present) |
| 265 | + int rr = rng() % rows; |
| 266 | + int cc = rng() % cols; |
| 267 | + int target_present = M[rr][cc]; |
| 268 | + run_test(M, target_present, true, "GenMatrix present (deterministic seed)"); |
| 269 | + |
| 270 | + // pick a value guaranteed absent: take last element and add 1 |
| 271 | + int last = M[rows - 1][cols - 1]; |
| 272 | + int target_absent = last + 1; |
| 273 | + run_test(M, target_absent, false, "GenMatrix absent (just above last)"); |
| 274 | + } |
| 275 | + } |
| 276 | +} |
| 277 | + |
| 278 | +// Main function |
| 279 | +int main() |
| 280 | +{ |
| 281 | + // Running the test cases |
| 282 | + test_cases(); |
| 283 | + |
| 284 | + return 0; |
| 285 | +} |
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