My Algorithm Summary
  • Introduction
  • Data Structure
    • Linked List
    • Stack
      • Monotone Stack
        • 42 Trapping Rain Water
        • 84 Largest Rectangle in Histogram
        • 85 Maximal Rectangle
        • 255 Verify Preorder Sequence in Binary Search Tree
        • 316 Remove Duplicate Characters
        • 402 Remove K Digits
        • 456 132 Pattern
        • 496 Next Greater Element I
        • 503 Next Greater Element II
      • 20 Valid Parentheses
      • 71 Simplify Path
      • 150 Evaluate Reverse Polish Notation
      • 155 Min Stack
      • 173 Binary Search Tree Iterator
      • 224 Basic Calculator
      • 227 Basic Calculator II
      • 232 Implement Queue using Stacks
      • 341 Flatten Nested List Iterator
      • 394 Decode String
      • 439 Ternary Expression Parser
      • 636 Exclusive Time of Functions
    • Heap
    • Trie
    • Segment Tree
    • Tree
      • 94 Binary Tree Inorder Traversal
      • 104 Maximum Depth of Binary Tree
      • 144 Binary Tree Preorder Traversal
      • 145 Binary Tree Postorder Traversal
      • 199 Binary Tree Right Side View
      • 226 Invert Binary Tree
      • 272 Closest Binary Search Tree Value II
      • 508 Most Frequent Subtree Sum
      • 513 Find Bottom Left Tree Value
      • 515 Find Largest Value in Each Tree Row
      • 617 Merge Two Binary Trees
      • 637 Average of Levels in Binary Tree
      • 653 Two Sum IV - Input is a BST
      • 654 Maximum Binary Tree
      • 669 Trim a Binary Search Tree
      • 666 Path Sum IV
      • 230 Kth Smallest Element in a BST
      • 250 Count Univalue Subtrees
      • 538 Convert BST to Greater Tree
      • 404 Sum of Left Leaves
      • 582 Kill Process
      • 112 Path Sum
      • 108 Convert Sorted Array to Binary Search Tree
      • 111 Minimum Depth of Binary Tree
      • 501 Find Mode in Binary Search Tree
      • 102 Binary Tree Level Order Traversal
      • 107 Binary Tree Level Order Traversal II
      • 103 Binary Tree Zigzag Level Order Traversal
      • 113 Path Sum II
      • 437 Path Sum III
      • 99 Recover Binary Search Tree
      • 687 Longest Univalue Path
      • 285 Inorder Successor in BST
      • 101 Symmetric Tree
      • 129 Sum Root to Leaf Numbers
      • 298 Binary Tree Longest Consecutive Sequence
      • 270 Closest Binary Search Tree Value
      • 549 Binary Tree Longest Consecutive Sequence II
      • 98 Validate Binary Search Tree
      • 652 Find Duplicate Subtrees
      • 314 Binary Tree Vertical Order Traversal
      • 333 Largest BST Subtree
      • 563 Binary Tree Tilt
      • 110 Balanced Binary Tree
    • Graph
      • Detect Cycle
  • Algorithms
    • Union Find
      • 695 Max Area of Island
      • 684 Redundant Connection
    • Binary Search
    • Topological Sorting
    • Breadth-First Search
      • 694 Number of Distinct Islands
    • Depth-First Search
    • Two Pointers
    • Sorting
    • Backtacking
    • Dynamic Programming
      • Interval DP
        • Matrix Chain Multiplication
        • Merge Stone
      • KnapSack Problem
        • 0-1 KnapSack
        • Unbounded KnapSack
      • Longest Increasing Subsequence
      • Longest Common Subsequence
    • Reservior Sampling
    • Bipartite Graph
      • Check Bipartite Graph
      • Maximal Matching - Hungarian Algorithm
    • String Pattern Matching
      • KMP Algorithm
      • Rabin Karp Algorithm
  • System Design
    • Consistent Hashing
    • Bloom Filter
    • Caching
      • LRU
      • LFU
    • Mini Twitter
    • Tiny Url
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On this page
  • Sorting
  • 1. Count Sort
  • 2. Bucket Sort
  • 3. Quick Sort
  • 4. Merge Sort
  • 5. Questions

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  1. Algorithms

Sorting

Sorting

1. Count Sort

Time: O(n), Space: O(n)

public static void countSort(int[] nums) {
    int max = Integer.MIN_VALUE, min = Integer.MAX_VALUE;

    for (int num : nums) {
        max = Math.max(max, num);
        min = Math.min(min, num);
    }

    int[] count = new int[max - min + 1];
    for (int num : nums) {
        ++count[num - min];
    }

    int index = 0;
    for (int i = min; i <= max; i++) {
        while (count[i - min] > 0) {
        nums[index] = i;
        ++index;
        --count[i - min];
        }
    }
}

2. Bucket Sort

Time: O(n + k), Space: O(n)

// To be continued

3. Quick Sort

Time: O(nlogn), Space: O(logn)

    public static void quickSort(int[] nums) {
        recQuickSort(nums, 0, nums.length - 1);
    }

    public static void recQuickSort(int[] nums, int start, int end) {
        if (start >= end) {
            return;
        }
        int pivot = partition(nums, start, end);
        recQuickSort(nums, start, pivot - 1);
        recQuickSort(nums, pivot + 1, end );
    }

    public static int partition(int[] nums, int start, int end) {
        // Use the middle element as pivot
        int pivot = (start + end) >> 1;
        // Put the pivot to the end
        swap(nums, pivot, end);

        // Put element smaller than the pivot to the first half of array
        for (int i = start; i < end; i++) {
            if (nums[i] < nums[end]) {
                swap(nums, i, start);
                ++start;
            }
        }
        // Put the pivot back to position it should be in after partition
        swap(nums, start, end);
        return start;
    }

    public static void swap(int[] nums, int i, int j) {
        int temp = nums[i];
        nums[i] = nums[j];
        nums[j] = temp;
    }
  • Quick Select (Find the Kth Largest/Smallest Number)

   public int quickSelect(int[] nums, int k, int start, int end) {
        int pivot = partition(nums, start, end);

        if (pivot < k) {
            return quickSelect(nums, k, pivot + 1, end);
        }
        else if (pivot > k) {
            return quickSelect(nums, k, start, pivot);
        }
        else {
            return nums[pivot];
        }
    }

    public int partition(int[] nums, int start, int end) {
        int mid = (start + end) >> 1;
        swap(nums, mid, end);

        for (int i = start; i < end; i++) {
            // To get the Kth largest number, put element larger than the pivot to the left 
            if (nums[i] > nums[end]) {
                swap(nums, start, i);
                ++start;
            }
        }
        swap(nums, start, end);
        return start;
    }

    public void swap(int[] nums, int i, int j) {
        int temp = nums[i];
        nums[i] = nums[j];
        nums[j] = temp;
    }

4. Merge Sort

Time: O(nlogn), Space: O(n)

    public void mergeSort(int[] nums) {
        if (nums == null || nums.length == 0) {
            return;
        }
        mergeSort(nums, 0, nums.length - 1);
    }

    public void mergeSort(int[] nums, int start, int end) {
        if (start >= end) {
            return;
        }

        int mid = (start + end) / 2;
        mergeSort(nums, start, mid);
        mergeSort(nums, mid + 1, end);
        merge(nums, start, mid, end);
    }

    public void merge(int[] nums, int start, int mid, int end) {
        int[] temp = new int[end - start + 1];
        int i = start;
        int j = mid + 1;
        int k = 0;

        while (i <= mid || j <= end) {
            if (i > mid || (j <= end && nums[j] < nums[i])) {
                temp[k++] = nums[j++];
            }
            else {
                temp[k++] = nums[i++];
            }
        }

        for (int index = 0; index < k; index++) {
            nums[start + index] = temp[index];
        }
    }

5. Questions

PreviousTwo PointersNextBacktacking

Last updated 5 years ago

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Merge Sort:

Quick Sort:

Count Sort: ,

Bucket Sort: ,

Reverse Pairs,
Kth Largest Element in an Array,
Wiggle Sort II
Sort Colors
H-Index
Sort Characters By Frequency,
Top K Frequent Elements
Maximum Gap