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Preface
- FAQ
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Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
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Integer Array
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Remove Element
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Zero Sum Subarray
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Subarray Sum K
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Subarray Sum Closest
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Recover Rotated Sorted Array
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Product of Array Exclude Itself
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Partition Array
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First Missing Positive
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2 Sum
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3 Sum
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3 Sum Closest
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Remove Duplicates from Sorted Array
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Remove Duplicates from Sorted Array II
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Merge Sorted Array
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Merge Sorted Array II
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Median
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Partition Array by Odd and Even
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Kth Largest Element
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Remove Element
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Binary Search
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First Position of Target
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Search Insert Position
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Search for a Range
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First Bad Version
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Search a 2D Matrix
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Search a 2D Matrix II
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Find Peak Element
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Search in Rotated Sorted Array
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Search in Rotated Sorted Array II
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Find Minimum in Rotated Sorted Array
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Find Minimum in Rotated Sorted Array II
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Median of two Sorted Arrays
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Sqrt x
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Wood Cut
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First Position of Target
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Math and Bit Manipulation
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Single Number
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Single Number II
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Single Number III
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O1 Check Power of 2
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Convert Integer A to Integer B
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Factorial Trailing Zeroes
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Unique Binary Search Trees
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Update Bits
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Fast Power
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Hash Function
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Happy Number
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Count 1 in Binary
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Fibonacci
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A plus B Problem
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Print Numbers by Recursion
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Majority Number
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Majority Number II
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Majority Number III
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Digit Counts
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Ugly Number
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Plus One
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Palindrome Number
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Task Scheduler
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Single Number
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Linked List
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Remove Duplicates from Sorted List
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Remove Duplicates from Sorted List II
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Remove Duplicates from Unsorted List
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Partition List
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Add Two Numbers
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Two Lists Sum Advanced
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Remove Nth Node From End of List
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Linked List Cycle
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Linked List Cycle II
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Reverse Linked List
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Reverse Linked List II
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Merge Two Sorted Lists
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Merge k Sorted Lists
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Reorder List
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Copy List with Random Pointer
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Sort List
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Insertion Sort List
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Palindrome Linked List
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LRU Cache
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Rotate List
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Swap Nodes in Pairs
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Remove Linked List Elements
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Remove Duplicates from Sorted List
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Binary Tree
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Binary Tree Preorder Traversal
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Binary Tree Inorder Traversal
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Binary Tree Postorder Traversal
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Binary Tree Level Order Traversal
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Binary Tree Level Order Traversal II
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Maximum Depth of Binary Tree
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Balanced Binary Tree
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Binary Tree Maximum Path Sum
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Lowest Common Ancestor
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Invert Binary Tree
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Diameter of a Binary Tree
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Construct Binary Tree from Preorder and Inorder Traversal
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Construct Binary Tree from Inorder and Postorder Traversal
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Subtree
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Binary Tree Zigzag Level Order Traversal
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Binary Tree Serialization
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Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
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Dynamic Programming
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Triangle
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Backpack
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Backpack II
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Minimum Path Sum
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Unique Paths
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Unique Paths II
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Climbing Stairs
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Jump Game
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Word Break
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Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
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Jump Game II
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Best Time to Buy and Sell Stock
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Best Time to Buy and Sell Stock II
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Best Time to Buy and Sell Stock III
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Best Time to Buy and Sell Stock IV
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Distinct Subsequences
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Interleaving String
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Maximum Subarray
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Maximum Subarray II
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Longest Increasing Continuous subsequence
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Longest Increasing Continuous subsequence II
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Maximal Square
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Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
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Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
-
Tags
Search for a Range
Question
- leetcode: Search for a Range | LeetCode OJ
- lintcode: (61) Search for a Range
Problem Statement
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
copy
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;
}
};
copy
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;
}
}
copy
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 .
Reference
- Binary Search – topcoder - 思路更清晰的 find first 模板,find last k 可转化为 find first
k + 1
谢谢 @mckelvin 同学推荐