// SIMULATION: Round 1, Problem A // "Find top K categories by total spending" // Input: transactions = [("food", 50), ("transport", 30), ("food", 20), // ("entertainment", 100), ("transport", 40)], K = 2 // Output: ["entertainment", "transport"] // Step 1: Think aloud — "I need to group by category, sum amounts, // then find top K. I'll use a HashMap + heap." // Step 2: State complexity — O(n + m log k) where n=transactions, m=categories // Step 3: Code fun topKCategories(transactions: List>, k: Int): List { // Group by category and sum val categoryTotals = transactions .groupBy { it.first } .mapValues { (_, txns) -> txns.sumOf { it.second } } // Use min-heap of size k for top-K val minHeap = PriorityQueue>(compareBy { it.second }) for ((category, total) in categoryTotals) { minHeap.offer(category to total) if (minHeap.size > k) minHeap.poll() } // Extract results in descending order return minHeap.sortedByDescending { it.second }.map { it.first } } // Step 4: Test // transactions = [("food",50), ("transport",30), ("food",20), // ("entertainment",100), ("transport",40)] // categoryTotals = {food:70, transport:70, entertainment:100} // After heap with k=2: [transport:70, entertainment:100] // Result: ["entertainment", "transport"] ✓ // Step 5: Edge cases // - Empty list → return empty // - K > number of categories → return all categories // - Tie in amounts → either is acceptable // SIMULATION: Round 1, Problem B // "Find all paths in binary tree that sum to target" fun pathSum(root: TreeNode?, target: Int): List> { val result = mutableListOf>() fun dfs(node: TreeNode?, remaining: Int, path: MutableList) { if (node == null) return path.add(node.value) if (node.left == null && node.right == null && remaining == node.value) { result.add(path.toList()) } dfs(node.left, remaining - node.value, path) dfs(node.right, remaining - node.value, path) path.removeAt(path.lastIndex) // Backtrack } dfs(root, target, mutableListOf()) return result } // Time: O(n), Space: O(h) where h = tree height

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