- 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 for a Range
Source
- leetcode: Search for a Range | LeetCode OJ
- lintcode: (61) Search for a Range
Problem
Given a sorted array of n integers, find the starting and ending position of a given target value.
If the target is not found in the array, return [-1, -1]
.
Example
Given [5, 7, 7, 8, 8, 10]
and target value 8
, return [3, 4]
.
Challenge
O(log n) time.
题解
Python
first/last position 结合。
class Solution:
"""
@param A : a list of integers
@param target : an integer to be searched
@return : a list of length 2, [index1, index2]
"""
def searchRange(self, A, target):
ret = [-1, -1]
if not A:
return ret
# find the first position of target
st, ed = 0, len(A) - 1
while st + 1 < ed:
mid = (st + ed) / 2
if A[mid] == target:
ed = mid
elif A[mid] < target:
st = mid
else:
ed = mid
if A[st] == target:
ret[0] = st
elif A[ed] == target:
ret[0] = ed
# find the last position of target
st, ed = 0, len(A) - 1
while st + 1 < ed:
mid = (st + ed) / 2
if A[mid] == target:
st = mid
elif A[mid] < target:
st = mid
else:
ed = mid
if A[ed] == target:
ret[1] = ed
elif A[st] == target:
ret[1] = st
return ret
C++
class Solution {
public:
vector<int> searchRange(vector<int>& nums, int target) {
vector<int> result = {-1, -1};
if (nums.empty()) return result;
int lb = -1, ub = nums.size();
while (lb + 1 < ub) {
int mid = lb + (ub - lb) / 2;
if (nums[mid] < target) lb = mid;
else ub = mid;
}
if ((ub < nums.size()) && (nums[ub] == target)) result[0] = ub;
else return result;
ub = nums.size();
while (lb + 1 < ub) {
int mid = lb + (ub - lb) / 2;
if (nums[mid] > target) ub = mid;
else lb = mid;
}
result[1] = ub - 1;
return result;
}
};
Java
lower/upper bound 的结合,做两次搜索即可。
public class Solution {
/**
*@param A : an integer sorted array
*@param target : an integer to be inserted
*return : a list of length 2, [index1, index2]
*/
public int[] searchRange(int[] A, int target) {
int[] result = new int[]{-1, -1};
if (A == null || A.length == 0) return result;
int lb = -1, ub = A.length;
// lower bound
while (lb + 1 < ub) {
int mid = lb + (ub - lb) / 2;
if (A[mid] < target) {
lb = mid;
} else {
ub = mid;
}
}
// whether A[lb + 1] == target, check lb + 1 first
if ((lb + 1 < A.length) && (A[lb + 1] == target)) {
result[0] = lb + 1;
} else {
result[0] = -1;
result[1] = -1;
// target is not in the array
return result;
}
// upper bound, since ub >= lb, we do not reset lb
ub = A.length;
while (lb + 1 < ub) {
int mid = lb + (ub - lb) / 2;
if (A[mid] > target) {
ub = mid;
} else {
lb = mid;
}
}
// target must exist in the array
result[1] = ub - 1;
return result;
}
}
源码分析
- 首先对输入做异常处理,数组为空或者长度为 0
- 分 lower/upper bound 两次搜索,注意如果在 lower bound 阶段未找到目标值时,upper bound 也一定找不到。
- 取
A[lb + 1]
时一定要注意判断索引是否越界!
复杂度分析
两次二分搜索,时间复杂度仍为 O(logn)O(\log n)O(logn).
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