LintCode & LeetCode
  • Introduction
  • Linked List
    • Sort List
    • Merge Two Sorted Lists
    • Merge k Sorted Lists
    • Linked List Cycle
    • Linked List Cycle II
    • Add Two Numbers II
    • Add Two Numbers
    • Odd Even Linked List
    • Intersection of Two Linked Lists
    • Reverse Linked List
    • Reverse Linked List II
    • Remove Linked List Elements
    • Remove Nth Node From End of List
    • Middle of the Linked List
    • Design Linked List
      • Design Singly Linked List
      • Design Doubly Linked List
    • Palindrome Linked List
    • Remove Duplicates from Sorted List
    • Remove Duplicates from Sorted List II
    • Implement Stack Using Singly Linked List
    • Copy List with Random Pointer
  • Binary Search
    • Search in Rotated Sorted Array
    • Search in Rotated Sorted Array II
    • Search in a Sorted Array of Unknown Size
    • First Bad Version
    • Find Minimum in Rotated Sorted Array
    • Find Minimum in Rotated Sorted Array II
    • Find Peak Element
    • Search for a Range
    • Find K Closest Elements
    • Search Insert Position
    • Peak Index in a Mountain Array
    • Heaters
  • Hash Table
    • Jewels and Stones
    • Single Number
    • Subdomain Visit Count
    • Design HashMap
    • Design HashSet
    • Logger Rate Limiter
    • Isomorphic Strings
    • Minimum Index Sum of Two Lists
    • Contains Duplicate II
    • Contains Duplicate III
    • Longest Consecutive Sequence
    • Valid Sudoku
    • Distribute Candies
    • Shortest Word Distance
    • Shortest Word Distance II
  • String
    • Rotate String
    • Add Binary
    • Implement strStr()
    • Longest Common Prefix
    • Reverse Words in a String
    • Reverse Words in a String II
    • Reverse Words in a String III
    • Valid Word Abbreviation
    • Group Anagrams
    • Unique Email Addresses
    • Next Closest Time
    • License Key Formatting
    • String to Integer - atoi
    • Ransom Note
    • Multiply Strings
    • Text Justification
    • Reorder Log Files
    • Most Common Word
    • Valid Parenthesis String
    • K-Substring with K different characters
    • Find All Anagrams in a String
    • Find the Closest Palindrome
    • Simplify Path
  • Array
    • Partition Array
    • Median of Two Sorted Arrays
    • Intersection of Two Arrays
    • Intersection of Two Arrays II
    • Maximum Subarray Sum
    • Minimum Subarray Sum
    • Maximum Subarray II
    • Maximum Subarray III
    • Subarray Sum Closest
    • Subarray Sum
    • Plus One
    • Maximum Subarray Difference
    • Maximum Subarray IV
    • Subarray Sum Equals K
    • Intersection of Two Arrays
    • Intersection of Two Arrays II
    • Find Pivot Index
    • Rotate Array
    • Get Smallest Nonnegative Integer Not In The Array
    • Maximize Distance to Closest Person
    • Sort Colors
    • Next Permutation
    • Rotate Image
    • Pour Water
    • Prison Cells After N Days
    • Majority Element
    • Can Place Flowers
    • Candy
  • Matrix
    • Spiral Matrix
    • Set Matrix Zeroes
    • Diagonal Traverse
  • Queue
    • Design Circular Queue
    • Implement Queue using Stacks
    • Implement Queue by Two Stacks
    • Implement Stack using Queues
    • Moving Average from Data Stream
    • Walls and Gates
    • Open the Lock
    • Sliding Window Maximum
    • Implement Queue Using Fixed Length Array
    • Animal Shelter
  • Stack
    • Valid Parentheses
    • Longest Valid Parentheses
    • Min Stack
    • Max Stack
    • Daily Temperatures
    • Evaluate Reverse Polish Notation
    • Next Greater Element I
    • Next Greater Element II
    • Next Greater Element III
    • Largest Rectangle in Histogram
    • Maximal Rectangle
    • Car Fleet
  • Heap
    • Trapping Rain Water II
    • The Skyline Problem
    • Top K Frequent Words
    • Top K Frequent Words II
    • Top K Frequent Elements
    • Top k Largest Numbers
    • Top k Largest Numbers II
    • Minimum Cost to Hire K Workers
    • Kth Largest Element in an Array
    • Kth Smallest Number in Sorted Matrix
    • Kth Smallest Sum In Two Sorted Arrays
    • K Closest Points to the Origin
    • Merge K Sorted Lists
    • Merge K Sorted Arrays
    • Top K Frequent Words - Map Reduce
  • Data Structure & Design
    • Hash Function
    • Heapify
    • LRU Cache
    • LFU Cache
    • Rehashing
    • Stack Sorting
    • Animal Shelter
    • Sliding Window Maximum
    • Moving Average from Data Stream
    • Find Median from Data Stream
    • Sliding Window Median
    • Design Hit Counter
    • Read N Characters Given Read4 II - Call multiple times
    • Read N Characters Given Read4
    • Flatten 2D Vector
    • Flatten Nested List Iterator
    • Design Search Autocomplete System
    • Time Based Key-Value Store
    • Design Tic-Tac-Toe
    • Insert Delete GetRandom O(1)
  • Union Find
    • Find the Connected Component in the Undirected Graph
    • Find the Weak Connected Component in the Directed Graph
    • Graph Valid Tree
    • Number of Islands
    • Number of Islands II
    • Surrounded Regions
    • Most Stones Removed with Same Row or Column
    • Redundant Connection
  • Trie
    • Implement Trie
    • Add and Search Word
    • Word Search II
    • Longest Word in Dictionary
    • Palindrome Pairs
    • Trie Serialization
    • Trie Service
    • Design Search Autocomplete System
    • Typeahead
  • Trees
    • Binary Tree Inorder Traversal
    • Binary Tree Postorder Traversal
    • Binary Tree Preorder Traversal
    • Binary Tree Level Order Traversal
    • Binary Tree Zigzag Level Order Traversal
    • Binary Tree Vertical Order Traversal
    • N-ary Tree Level Order Traversal
    • N-ary Tree Preorder Traversal
    • N-ary Tree Postorder Traversal
    • Construct Binary Tree from Preorder and Inorder Traversal
    • Populating Next Right Pointers in Each Node
    • Populating Next Right Pointers in Each Node II
    • Maximum Depth of Binary Tree
    • Symmetric Tree
    • Validate Binary Search Tree
    • Convert Sorted Array to Binary Search Tree
    • Path Sum
    • Path Sum II
    • Path Sum III
    • Binary Tree Maximum Path Sum
    • Kth Smallest Element in a BST
    • Same Tree
    • Lowest Common Ancestor of a Binary Tree
    • Lowest Common Ancestor of a Binary Search Tree
    • Nested List Weight Sum II
    • BST Node Distance
    • Minimum Distance (Difference) Between BST Nodes
    • Closet Common Manager
    • N-ary Tree Postorder Traversal
    • Serialize and Deserialize Binary Tree
    • Serialize and Deserialize N-ary Tree
    • Diameter of a Binary Tree
    • Print Binary Trees
  • Segment Tree
    • Segment Tree Build
    • Range Sum Query - Mutable
  • Binary Indexed Tree
  • Graph & Search
    • Clone Graph
    • N Queens
    • Six Degrees
    • Number of Islands
    • Number of Distinct Islands
    • Word Search
    • Course Schedule
    • Course Schedule II
    • Word Ladder
    • Redundant Connection
    • Redundant Connection II
    • Longest Increasing Path in a Matrix
    • Reconstruct Itinerary
    • The Maze
    • The Maze II
    • The Maze III
    • Topological Sorting
    • Island Perimeter
    • Flood Fill
    • Cheapest Flights Within K Stops
    • Evaluate Division
    • Alien Dictionary
    • Cut Off Trees for Golf Event
    • Jump Game II
    • Most Stones Removed with Same Row or Column
  • Backtracking
    • Subsets
    • Subsets II
    • Letter Combinations of a Phone Number
    • Permutations
    • Permutations II
    • Combinations
    • Combination Sum
    • Combination Sum II
    • Combination Sum III
    • Combination Sum IV
    • N-Queens
    • N-Queens II
    • Generate Parentheses
    • Subsets of Size K
  • Two Pointers
    • Two Sum II
    • Triangle Count
    • Trapping Rain Water
    • Container with Most Water
    • Minimum Size Subarray Sum
    • Minimum Window Substring
    • Longest Substring Without Repeating Characters
    • Longest Substring with At Most K Distinct Characters
    • Longest Substring with At Most Two Distinct Characters
    • Fruit Into Baskets
    • Nuts & Bolts Problem
    • Valid Palindrome
    • The Smallest Difference
    • Reverse String
    • Remove Element
    • Max Consecutive Ones
    • Max Consecutive Ones II
    • Remove Duplicates from Sorted Array
    • Remove Duplicates from Sorted Array II
    • Move Zeroes
    • Longest Repeating Character Replacement
    • 3Sum With Multiplicity
    • Merge Sorted Array
    • 3Sum Smaller
    • Backspace String Compare
  • Mathematics
    • Ugly Number
    • Ugly Number II
    • Super Ugly Number
    • Sqrt(x)
    • Random Number 1 to 7 With Equal Probability
    • Pow(x, n)
    • Narcissistic Number
    • Rectangle Overlap
    • Happy Number
    • Add N Days to Given Date
    • Reverse Integer
    • Greatest Common Divisor or Highest Common Factor
  • Bit Operation
    • IP to CIDR
  • Random
    • Random Pick with Weight
    • Random Pick Index
    • Linked List Random Node
  • Dynamic Programming
    • House Robber
    • House Robber II
    • House Robber III
    • Longest Increasing Continuous Subsequence
    • Longest Increasing Continuous Subsequence II
    • Coins in a Line
    • Coins in a Line II
    • Coins in a Line III
    • Maximum Product Subarray
    • Longest Palindromic Substring
    • Stone Game
    • Burst Balloons
    • Perfect Squares
    • Triangle
    • Pascal's Triangle
    • Pascal's Triangle II
    • Min Cost Climbing Stairs
    • Climbing Stairs
    • Unique Paths
    • Unique Paths II
    • Minimum Path Sum
    • Word Break
    • Word Break II
    • Range Sum Query - Immutable
    • Decode Ways
    • Edit Distance
    • Unique Binary Search Trees
    • Unique Binary Search Trees II
    • Maximal Rectangle
    • Maximal Square
    • Regular Expression Matching
    • Wildcard Matching
    • Flip Game II
    • Longest Increasing Subsequence
    • Target Sum
    • Partition Equal Subset Sum
    • Coin Change
    • Jump Game
    • Can I Win
    • Maximum Sum Rectangle in a 2D Matrix
    • Cherry Pick
  • Knapsack
    • Backpack
    • Backpack II
    • Backpack III
    • Backpack IV
    • Backpack V
    • Backpack VI
    • Backpack VII
    • Coin Change
    • Coin Change II
  • High Frequency
    • 2 Sum Closest
    • 3 Sum
    • 3 Sum Closest
    • Sort Colors II
    • Majority Number
    • Majority Number II
    • Majority Number III
    • Best Time to Buy and Sell Stock
    • Best Time to Buy and Sell Stock II
    • Best Time to Buy and Sell Stock III
    • Best Time to Buy and Sell Stock IV
    • Two Sum
    • Two Sum II - Input array is sorted
    • Two Sum III - Data structure design
    • Two Sum IV - Input is a BST
    • 4 Sum
    • 4 Sum II
  • Sorting
  • Greedy
    • Jump Game II
    • Remove K Digits
  • Minimax
    • Nim Game
    • Can I Win
  • Sweep Line & Interval
    • Meeting Rooms
    • Meeting Rooms II
    • Merge Intervals
    • Insert Interval
    • Number of Airplanes in the Sky
    • Exam Room
    • Employee Free Time
    • Closest Pair of Points
    • My Calendar I
    • My Calendar II
    • My Calendar III
    • Add Bold Tag in String
  • Other Algorithms and Data Structure
    • Huffman Coding
    • Reservoir Sampling
    • Bloom Filter
    • External Sorting
    • Construct Quad Tree
  • Company Tag
    • Google
      • Guess the Word
      • Raindrop on Sidewalk
    • Airbnb
      • Display Pages (Pagination)
    • Amazon
  • Problem Solving Summary
    • String or Array Rotation
    • Tips for Avoiding Bugs
    • Substring or Subarray Search
    • Sliding Window
    • K Sums
    • Combination Sum Series
    • Knapsack Problems
    • Depth-first Search
    • Large Number Operation
    • Implementation - Simulation
    • Monotonic Stack & Queue
    • Top K Problems
    • Java Interview Tips
      • OOP in Java
      • Conversion in Java
      • Data Structures in Java
    • Algorithm Optimization Tips
  • Reference
Powered by GitBook
On this page
  • Question
  • Analysis
  • Solution
  • Reference

Was this helpful?

  1. Heap

Top k Largest Numbers

Question

Given an integer array, find the top k largest numbers in it.

Example

Given [3,10,1000,-99,4,100] and k = 3. Return [1000, 100, 10].

Analysis

此题运用Priority Queue,也就是Max Heap来求解十分简洁。重点在于对该数据结构的理解和应用。 使用Max Heap,也就是保证了堆顶的元素是整个堆最大的那一个。第一步,先构建一个capacity为k的Max Heap,这需要O(n)时间;第二步,对这个Max Heap进行k次poll()操作,丛中取出k个maximum的元素,这一步操作需要O(k logn)。因此最终整个操作的时间复杂度是 O(n + k logn),空间复杂度是O(k)

对于Top k largest(or smallest) elements in an array问题,有如下几种常见方法:

  1. 冒泡排序k次

  2. 使用临时数组

  3. 使用排序

  4. 使用最大堆

  5. 使用排序统计

  6. 使用最小堆

(摘自GeeksForGeeks)

Method 1. Use bubble k times

Thanks to Shailendra for suggesting this approach.

1) Modify Bubble Sort to run the outer loop at most k times.

2) Print the last k elements of the array obtained in step 1.

Time Complexity: O(nk)

Like Bubble sort, other sorting algorithms like Selection Sort can also be modified to get the k largest elements.

Method 2. Use temporary array

K largest elements from arr[0..n-1]

1) Store the first k elements in a temporary array temp[0..k-1].

2) Find the smallest element in temp[], let the smallest element be min.

3) For each element x in arr[k] to arr[n-1] If x is greater than the min then remove min from temp[] and insert x.

4) Print final k elements of temp[]

Time Complexity: O((n-k) k). If we want the output sorted then O((n-k) k + klogk)

Method 3. Use sorting

1) Sort the elements in descending order in O(nLogn)

2) Print the first k numbers of the sorted array O(k).

Time complexity: O(nlogn)

Method 4. Use Max heap

1) Build a Max Heap tree in O(n)

2) Use Extract Max k times to get k maximum elements from the Max Heap O(klogn)

Time complexity: O(n + klogn)

Method 5. Use Order Statistics

1) Use order statistic algorithm to find the kth largest element. Please see the topic selection in worst-case linear time O(n) 2) Use QuickSort Partition algorithm to partition around the kth largest number O(n). 3) Sort the k-1 elements (elements greater than the kth largest element) O(kLogk). This step is needed only if sorted output is required.

Time complexity: O(n) if we don’t need the sorted output, otherwise O(n+kLogk)

Thanks to Shilpi for suggesting the first two approaches.

Method 6. Use Min Heap

This method is mainly an optimization of method 2 temp array. Instead of using temp[] array, use Min Heap.

Thanks to geek4u for suggesting this method.

1) Build a Min Heap MH of the first k elements (arr[0] to arr[k-1]) of the given array. O(k)

2) For each element, after the kth element (arr[k] to arr[n-1]), compare it with root of MH. ……a) If the element is greater than the root then make it root and call heapify for MH ……b) Else ignore it.

The step 2 is O((n-k) * logk)

3) Finally, MH has k largest elements and root of the MH is the kth largest element.

Time Complexity: O(k + (n-k)Logk) without sorted output. If sorted output is needed then O(k + (n-k)Logk + kLogk)

All of the above methods can also be used to find the kth largest (or smallest) element.

Solution

Max Heap

class Solution {
    /*
     * @param nums an integer array
     * @param k an integer
     * @return the top k largest numbers in array
     */
     public int[] topk(int[] nums, int k) {
         Comparator<Integer> comparator = new Comparator<Integer>() {
             @Override
             public int compare(Integer o1, Integer o2) {
                 if(o1 < o2) {
                     return 1;
                 } else if(o1 > o2) {
                     return -1;
                 } else {
                     return 0;
                 }
             }
         };
         PriorityQueue<Integer> minheap = new PriorityQueue<Integer>(k, comparator);

         for (int i : nums) {
             minheap.add(i);
         }

         int[] result = new int[k];
         for (int i = 0; i < result.length; i++) {
             result[i] = minheap.poll();
         }
         return result;
    }
};

Min Heap

class Solution {
    /*
     * @param nums an integer array
     * @param k an integer
     * @return the top k largest numbers in array
     */
    public int[] topk(int[] nums, int k) {
        PriorityQueue<Integer> minheap = new PriorityQueue<Integer>();
        for (int i = 0; i < k; i++) {
           minheap.offer(nums[i]);
        }
        for (int i = k; i < nums.length; i++) {
            if (nums[i] > minheap.peek()) {
                minheap.poll();
                minheap.offer(nums[i]);
            }
        }
        Iterator it = minheap.iterator();
        List<Integer> result = new ArrayList<Integer>();
        while(it.hasNext()) {
            result.add((Integer) it.next());
        }
        Collections.sort(result, Collections.reverseOrder());

        int[] res = new int[result.size()];
        Iterator<Integer> iter = result.iterator();
        for (int i = 0; i < res.length; i++) {
            res[i] = iter.next().intValue();
        }
        return res;
    }
};

Sorting

class Solution {
    /*
     * @param nums an integer array
     * @param k an integer
     * @return the top k largest numbers in array
     */
    public int[] topk(int[] nums, int k) {
        Arrays.sort(nums);
        int[] result = new int[k];
        int j = 0;
        for (int i = nums.length - 1; i > nums.length - k - 1; i--) {
            result[j] = nums[i];
            j++;
        }
        return result;
    }
};

Reference

PreviousTop K Frequent ElementsNextTop k Largest Numbers II

Last updated 5 years ago

Was this helpful?

基于堆实现的优先级队列:PriorityQueue 解决 Top K 问题
geeksforgeeks.org: k largest(or smallest) elements in an array
My Favorite Interview Question by Arden Dertat - "In an integer array with N elements (N is large), find the minimum k elements (k << N)."