-
Preface
- FAQ
-
Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
-
Part II - Coding
- String
-
Integer Array
-
Remove Element
-
Zero Sum Subarray
-
Subarray Sum K
-
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
-
Partition Array by Odd and Even
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Kth Largest Element
-
Remove Element
-
Binary Search
-
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
-
Wood Cut
-
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
-
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
-
Ugly Number
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Plus One
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Palindrome Number
-
Task Scheduler
-
Single Number
-
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
-
Triangle
-
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
-
Jump Game
-
Word Break
-
Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
-
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
-
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
-
Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
-
Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
-
Tags
First Position of Target
Question
- lintcode: First Position of Target
Problem Statement
For a given sorted array (ascending order) and a target
number, find the
first index of this number in O(log n)
time complexity.
If the target number does not exist in the array, return -1
.
Example
If the array is [1, 2, 3, 3, 4, 5, 10]
, for given target 3
, return 2
.
Challenge
If the count of numbers is bigger than , can your code work properly?
题解
对于已排序升序(升序)数组,使用二分查找可满足复杂度要求,注意数组中可能有重复值,所以需要使用类似lower_bound
中提到的方法。
Java
class Solution {
/**
* @param nums: The integer array.
* @param target: Target to find.
* @return: The first position of target. Position starts from 0.
*/
public int binarySearch(int[] nums, int target) {
if (nums == null || nums.length == 0) {
return -1;
}
int start = -1, end = nums.length;
int mid;
while (start + 1 < end) {
// avoid overflow when (end + start)
mid = start + (end - start) / 2;
if (nums[mid] < target) {
start = mid;
} else {
end = mid;
}
}
if (end == nums.length || nums[end] != target) {
return -1;
} else {
return end;
}
}
}
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。
- 初始化
start, end, mid
三个变量,这里start
初始化为-1
主要是考虑到end
为1
。注意mid的求值方法,可以防止两个整型值相加时溢出。 - 使用迭代而不是递归进行二分查找,因为工程中递归写法存在潜在溢出的可能。
- while终止条件应为
start + 1 < end
而不是start <= end
,start == end
时可能出现死循环。即循环终止条件是相邻或相交元素时退出。由于这里初始化时start < end
,所以一定是start + 1 == end
时退出循环。 - 迭代终止时有两种情况,一种是在原数组中找到了,这种情况下一定是
end
, 因为start
的更新只在nums[mid] < target
. - 最后判断
end
和target
的关系,先排除end
为数组长度这种会引起越界的情况,然后再判断和目标值是否相等。
复杂度分析
时间复杂度 , 空间复杂度 . 对于题中的 follow up, Java 中数组不允许使用 long 型,如果使用 long 型,那么数组大小可大 17GB 之巨!!几乎没法用。
Reference
- 《挑战程序设计竞赛》3.1节