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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
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Tags
Climbing Stairs
Question
- lintcode: (111) Climbing Stairs
You are climbing a stair case. It takes n steps to reach to the top.
Each time you can either climb 1 or 2 steps.
In how many distinct ways can you climb to the top?
Example
Given an example n=3 , 1+1+1=2+1=1+2=3
return 3
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题解
题目问的是到达顶端的方法数,我们采用序列类问题的通用分析方法,可以得到如下四要素:
- State: f[i] 爬到第i级的方法数
- Function: f[i]=f[i-1]+f[i-2]
- Initialization: f[0]=1,f[1]=1
- Answer: f[n]
尤其注意状态转移方程的写法,f[i]只可能由两个中间状态转化而来,一个是f[i-1],由f[i-1]到f[i]其方法总数并未增加;另一个是f[i-2],由f[i-2]到f[i]隔了两个台阶,因此有1+1和2两个方法,因此容易写成 f[i]=f[i-1]+f[i-2]+1,但仔细分析后能发现,由f[i-2]到f[i]的中间状态f[i-1]已经被利用过一次,故f[i]=f[i-1]+f[i-2]. 使用动规思想解题时需要分清『重叠子状态』, 如果有重复的需要去重。
C++
class Solution {
public:
/**
* @param n: An integer
* @return: An integer
*/
int climbStairs(int n) {
if (n < 1) {
return 0;
}
vector<int> ret(n + 1, 1);
for (int i = 2; i != n + 1; ++i) {
ret[i] = ret[i - 1] + ret[i - 2];
}
return ret[n];
}
};
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- 异常处理
- 初始化n+1个元素,初始值均为1。之所以用n+1个元素是下标分析起来更方便
- 状态转移方程
- 返回ret[n]
初始化ret[0]也为1,可以认为到第0级也是一种方法。
以上答案的空间复杂度为 ,仔细观察后可以发现在状态转移方程中,我们可以使用三个变量来替代长度为n+1的数组。具体代码可参考 climbing-stairs | 九章算法
Python
class Solution:
def climbStairs(n):
if n < 1:
return 0
l = r = 1
for _ in xrange(n - 1):
l, r = r, r + l
return r
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C++
class Solution {
public:
/**
* @param n: An integer
* @return: An integer
*/
int climbStairs(int n) {
if (n < 1) {
return 0;
}
int ret0 = 1, ret1 = 1, ret2 = 1;
for (int i = 2; i != n + 1; ++i) {
ret0 = ret1 + ret2;
ret2 = ret1;
ret1 = ret0;
}
return ret0;
}
};
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