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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
Print Numbers by Recursion
Question
- lintcode: (371) Print Numbers by Recursion
Print numbers from 1 to the largest number with N digits by recursion.
Example
Given N = 1, return [1,2,3,4,5,6,7,8,9].
Given N = 2, return [1,2,3,4,5,6,7,8,9,10,11,12,...,99].
Note
It's pretty easy to do recursion like:
recursion(i) {
if i > largest number:
return
results.add(i)
recursion(i + 1)
}
however this cost a lot of recursion memory as the recursion depth maybe very large.
Can you do it in another way to recursive with at most N depth?
Challenge
Do it in recursion, not for-loop.
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题解
从小至大打印 N 位的数列,正如题目中所提供的 recursion(i)
, 解法简单粗暴,但问题在于 N 稍微大一点时栈就溢出了,因为递归深度太深了。能联想到的方法大概有两种,一种是用排列组合的思想去解释,把0~9当成十个不同的数(字符串表示),塞到 N 个坑位中,这个用 DFS 来解应该是可行的;另一个则是使用数学方法,依次递归递推,比如 N=2 可由 N=1递归而来,具体方法则是乘10进位加法。题中明确要求递归深度最大不超过 N, 故 DFS 方法比较危险。
Java
public class Solution {
/**
* @param n: An integer.
* return : An array storing 1 to the largest number with n digits.
*/
public List<Integer> numbersByRecursion(int n) {
List<Integer> result = new ArrayList<Integer>();
if (n <= 0) {
return result;
}
helper(n, result);
return result;
}
private void helper(int n, List<Integer> ret) {
if (n == 0) return;
helper(n - 1, ret);
// current base such as 10, 20, 30...
int base = (int)Math.pow(10, n - 1);
// get List size before for loop
int size = ret.size();
for (int i = 1; i < 10; i++) {
// add 10, 100, 1000...
ret.add(i * base);
for (int j = 0; j < size; j++) {
// add 11, 12, 13...
ret.add(ret.get(j) + base * i);
}
}
}
}
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源码分析
递归步的截止条件n == 0
, 由于需要根据之前 N-1 位的数字递推,base
每次递归一层都需要乘10,size
需要在for
循环之前就确定。
复杂度分析
添加 个元素,时间复杂度 , 空间复杂度 .