- Preface
- FAQ
- Guidelines for Contributing
- Contributors
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- 算法复习——排序
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bucket Sort
- Counting Sort
- Radix Sort
- Basics Algorithm
- Divide and Conquer
- Binary Search
- Math
- Greatest Common Divisor
- Prime
- Knapsack
- Probability
- Shuffle
- Bitmap
- Basics Misc
- Bit Manipulation
- Part II - Coding
- String
- strStr
- Two Strings Are Anagrams
- Compare Strings
- Anagrams
- Longest Common Substring
- Rotate String
- Reverse Words in a String
- Valid Palindrome
- Longest Palindromic Substring
- Space Replacement
- Wildcard Matching
- Length of Last Word
- Count and Say
- Integer Array
- Remove Element
- Zero Sum Subarray
- Subarray Sum K
- Subarray Sum Closest
- Recover Rotated Sorted Array
- Product of Array Exclude Itself
- Partition Array
- First Missing Positive
- 2 Sum
- 3 Sum
- 3 Sum Closest
- Remove Duplicates from Sorted Array
- Remove Duplicates from Sorted Array II
- Merge Sorted Array
- Merge Sorted Array II
- Median
- Partition Array by Odd and Even
- Kth Largest Element
- Binary Search
- Binary Search
- Search Insert Position
- Search for a Range
- First Bad Version
- Search a 2D Matrix
- Search a 2D Matrix II
- Find Peak Element
- Search in Rotated Sorted Array
- Search in Rotated Sorted Array II
- Find Minimum in Rotated Sorted Array
- Find Minimum in Rotated Sorted Array II
- Median of two Sorted Arrays
- Sqrt x
- Wood Cut
- Math and Bit Manipulation
- Single Number
- Single Number II
- Single Number III
- O1 Check Power of 2
- Convert Integer A to Integer B
- Factorial Trailing Zeroes
- Unique Binary Search Trees
- Update Bits
- Fast Power
- Hash Function
- Count 1 in Binary
- Fibonacci
- A plus B Problem
- Print Numbers by Recursion
- Majority Number
- Majority Number II
- Majority Number III
- Digit Counts
- Ugly Number
- Plus One
- Linked List
- Remove Duplicates from Sorted List
- Remove Duplicates from Sorted List II
- Remove Duplicates from Unsorted List
- Partition List
- Add Two Numbers
- Two Lists Sum Advanced
- Remove Nth Node From End of List
- Linked List Cycle
- Linked List Cycle II
- Reverse Linked List
- Reverse Linked List II
- Merge Two Sorted Lists
- Merge k Sorted Lists
- Reorder List
- Copy List with Random Pointer
- Sort List
- Insertion Sort List
- Palindrome Linked List
- Delete Node in the Middle of Singly Linked List
- Rotate List
- Swap Nodes in Pairs
- Remove Linked List Elements
- Binary Tree
- Binary Tree Preorder Traversal
- Binary Tree Inorder Traversal
- Binary Tree Postorder Traversal
- Binary Tree Level Order Traversal
- Binary Tree Level Order Traversal II
- Maximum Depth of Binary Tree
- Balanced Binary Tree
- Binary Tree Maximum Path Sum
- Lowest Common Ancestor
- Invert Binary Tree
- Diameter of a Binary Tree
- Construct Binary Tree from Preorder and Inorder Traversal
- Construct Binary Tree from Inorder and Postorder Traversal
- Subtree
- Binary Tree Zigzag Level Order Traversal
- Binary Tree Serialization
- Binary Search Tree
- Insert Node in a Binary Search Tree
- Validate Binary Search Tree
- Search Range in Binary Search Tree
- Convert Sorted Array to Binary Search Tree
- Convert Sorted List to Binary Search Tree
- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
- Unique Subsets
- Permutations
- Unique Permutations
- Next Permutation
- Previous Permuation
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Unique Binary Search Trees II
- Palindrome Partitioning
- Combinations
- Combination Sum
- Combination Sum II
- Minimum Depth of Binary Tree
- Word Search
- Dynamic Programming
- Triangle
- Backpack
- Backpack II
- Minimum Path Sum
- Unique Paths
- Unique Paths II
- Climbing Stairs
- Jump Game
- Word Break
- Longest Increasing Subsequence
- Follow up
- Palindrome Partitioning II
- Longest Common Subsequence
- Edit Distance
- Jump Game II
- 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
- Distinct Subsequences
- Interleaving String
- Maximum Subarray
- Maximum Subarray II
- Longest Increasing Continuous subsequence
- Longest Increasing Continuous subsequence II
- Maximal Square
- Graph
- Find the Connected Component in the Undirected Graph
- Route Between Two Nodes in Graph
- Topological Sorting
- Word Ladder
- Bipartial Graph Part I
- Data Structure
- Implement Queue by Two Stacks
- Min Stack
- Sliding Window Maximum
- Longest Words
- Heapify
- Problem Misc
- Nuts and Bolts Problem
- String to Integer
- Insert Interval
- Merge Intervals
- Minimum Subarray
- Matrix Zigzag Traversal
- Valid Sudoku
- Add Binary
- Reverse Integer
- Gray Code
- Find the Missing Number
- Minimum Window Substring
- Continuous Subarray Sum
- Continuous Subarray Sum II
- Longest Consecutive Sequence
- Part III - Contest
- Google APAC
- APAC 2015 Round B
- Problem A. Password Attacker
- APAC 2016 Round D
- Problem A. Dynamic Grid
- Microsoft
- Microsoft 2015 April
- Problem A. Magic Box
- Problem B. Professor Q's Software
- Problem C. Islands Travel
- Problem D. Recruitment
- Microsoft 2015 April 2
- Problem A. Lucky Substrings
- Problem B. Numeric Keypad
- Problem C. Spring Outing
- Microsoft 2015 September 2
- Problem A. Farthest Point
- Appendix I Interview and Resume
- Interview
- Resume
- 術語表
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Search a 2D Matrix II
Source
- leetcode: Search a 2D Matrix II | LeetCode OJ
- lintcode: (38) Search a 2D Matrix II
Problem
Write an efficient algorithm that searches for a value in an m x n matrix, return the occurrence of it.
This matrix has the following properties:
- Integers in each row are sorted from left to right.
- Integers in each column are sorted from up to bottom.
- No duplicate integers in each row or column.
Example
Consider the following matrix:
[1, 3, 5, 7],
[2, 4, 7, 8],
[3, 5, 9, 10]
Given target = 3 , return 2 .
Challenge
O(m+n) time and O(1) extra space
题解 - 自右上而左下
- 复杂度要求——O(m+n) time and O(1) extra space,同时输入只满足自顶向下和自左向右的升序,行与行之间不再有递增关系,与上题有较大区别。时间复杂度为线性要求,因此可从元素排列特点出发,从一端走向另一端无论如何都需要 m+n 步,因此可分析对角线元素。
- 首先分析如果从左上角开始搜索,由于元素升序为自左向右和自上而下,因此如果 target 大于当前搜索元素时还有两个方向需要搜索,不太合适。
- 如果从右上角开始搜索,由于左边的元素一定不大于当前元素,而下面的元素一定不小于当前元素,因此每次比较时均可排除一列或者一行元素(大于当前元素则排除当前行,小于当前元素则排除当前列,由矩阵特点可知),可达到题目要求的复杂度。
在遇到之前没有遇到过的复杂题目时,可先使用简单的数据进行测试去帮助发现规律。
Python
class Solution:
"""
@param matrix: An list of lists of integers
@param target: An integer you want to search in matrix
@return: An integer indicates the total occurrence of target in the given matrix
"""
def searchMatrix(self, matrix, target):
if not matrix or not matrix[0]:
return 0
occur = 0
row, col = 0, len(matrix[0])
while row < len(matrix) and col >= 0:
if matrix[row][col] == target:
occur += 1
col -= 1
elif matrix[row][col] < target:
row += 1
else:
col -= 1
return occur
C++
class Solution {
public:
/**
* @param matrix: A list of lists of integers
* @param target: An integer you want to search in matrix
* @return: An integer indicate the total occurrence of target in the given matrix
*/
int searchMatrix(vector<vector<int> > &matrix, int target) {
if (matrix.empty() || matrix[0].empty()) {
return 0;
}
const int ROW = matrix.size();
const int COL = matrix[0].size();
int row = 0, col = COL - 1;
int occur = 0;
while (row < ROW && col >= 0) {
if (target == matrix[row][col]) {
++occur;
--col;
} else if (target < matrix[row][col]){
--col;
} else {
++row;
}
}
return occur;
}
};
Java
public class Solution {
/**
* @param matrix: A list of lists of integers
* @param: A number you want to search in the matrix
* @return: An integer indicate the occurrence of target in the given matrix
*/
public int searchMatrix(int[][] matrix, int target) {
int occurrence = 0;
if (matrix == null || matrix[0] == null) {
return occurrence;
}
int row = 0, col = matrix[0].length - 1;
while (row >= 0 && row < matrix.length && col >= 0 && col < matrix[0].length) {
if (matrix[row][col] == target) {
occurrence++;
col--;
} else if (matrix[row][col] > target) {
col--;
} else {
row++;
}
}
return occurrence;
}
}
源码分析
- 首先对输入做异常处理,不仅要考虑到 matrix 为空串,还要考虑到 matrix[0]也为空串。
- 注意循环终止条件。
- 在找出
target
后应继续向左搜索其他可能相等的元素,下方比当前元素大,故排除此列。
严格来讲每次取二维矩阵元素前都应该进行 null 检测。
复杂度分析
由于每行每列遍历一次,故时间复杂度为 O(m+n)O(m + n)O(m+n).
Reference
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