算法题是程序员的基本功,也是各个大厂必考察的重点,让我们一起坚持写算法题吧
遇事不决,可问春风,春风不语,即是本心。
我们在我们能力范围内,做好我们该做的事,然后相信一切都事最好的安排就可以啦,慢慢来,会很快,向前走,别回头。
目录
思路:首先想到的是暴力双层循环,时间复杂度过高,可以使用哈希表,空间换时间,降低时间复杂度。利用hash表,时间复杂度O(n),存储当前值与下标,每次判断目标值-当前值是否存在,若存在,则找出。
java版:
class Solution {
public int[] twoSum(int[] nums, int target) {
Map<Integer, Integer> map = new HashMap<>() ;
for(int i=0; i<nums.length; i++){
int element = target - nums[i] ;
if(!map.containsKey(element)){
map.put(nums[i], i) ;
}else{
return new int []{map.get(element), i} ;
}
}
return new int []{} ;
}
}
python版:
?
class Solution:
def twoSum(self, nums: List[int], target: int) -> List[int]:
res = []
d = {}
index = 0
for element in nums:
if d.get(target - element) == None:
d[element] = index
else:
res.append(d[target - element])
res.append(index)
return res
index += 1
return res
JS版:
?
/**
* @param {number[]} nums
* @param {number} target
* @return {number[]}
*/
var twoSum = function(nums, target) {
const obj = {}
const ans = []
for(let i=0; i<nums.length; i++){
let element = target - nums[i]
if(obj[element] === undefined){
obj[nums[i]] = i
}else{
ans.push(obj[element])
ans.push(i)
return ans
}
}
return ans
};
思路:遍历链表,求和后,拼接即可,取余保留到当前,取整用于进位。
Java版:
/**
* Definition for singly-linked list.
* public class ListNode {
* int val;
* ListNode next;
* ListNode() {}
* ListNode(int val) { this.val = val; }
* ListNode(int val, ListNode next) { this.val = val; this.next = next; }
* }
*/
class Solution {
public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
ListNode node = new ListNode(0) ;
ListNode pre = node ;
int carry = 0 ;
while(l1 != null || l2 != null){
int x = l1 == null ? 0 : l1.val ;
int y = l2 == null ? 0 : l2.val ;
int sum = x + y + carry ;
carry = sum / 10 ;
sum = sum % 10 ;
ListNode node1 = new ListNode(sum) ;
if(l1 != null){
l1 = l1.next ;
}
if(l2 != null){
l2 = l2.next ;
}
node.next = node1 ;
node = node.next ;
}
if(carry != 0){
node.next = new ListNode(carry) ;
}
return pre.next ;
}
}
Python版:
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def addTwoNumbers(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:
node = ListNode(0)
pre = node
carry = 0
while (l1 or l2):
x = y = 0
if l1:
x = l1.val
if l2:
y = l2.val
sum = x + y + carry
# python与java不一样,这里需要用整除才行
carry = sum // 10
sum = sum % 10
if l1 != None:
l1 = l1.next
if l2 != None:
l2 = l2.next
node.next = ListNode(sum)
node = node.next
if carry > 0:
node.next = ListNode(carry)
node = node.next
return pre.next
JS版:
/**
* Definition for singly-linked list.
* function ListNode(val, next) {
* this.val = (val===undefined ? 0 : val)
* this.next = (next===undefined ? null : next)
* }
*/
/**
* @param {ListNode} l1
* @param {ListNode} l2
* @return {ListNode}
*/
var addTwoNumbers = function(l1, l2) {
node = new ListNode(0)
pre = node
let carry = 0
while(l1 != null || l2 != null){
const x = l1 == null ? 0 : l1.val
const y = l2 == null ? 0 : l2.val
let sum = x + y + carry
// js与python都是弱类型的,需要取整
carry = Math.floor(sum / 10)
sum = sum % 10
if(l1 != null){
l1 = l1.next
}
if(l2 != null){
l2 = l2.next
}
node.next = new ListNode(sum)
node = node.next
}
if(carry > 0){
node.next = new ListNode(carry)
node = node.next
}
return pre.next
};
思路:可以用队列实现,队列满足先进先出的特性,如果队列中不含有当前元素,则元素入队,如果队列中含有当前元素,则出队直至不含有当前元素,整个过程中队列的最大长度即是答案。
Java版:
class Solution {
public int lengthOfLongestSubstring(String s) {
LinkedList<Character> queue = new LinkedList<>() ;
int max = 0 ;
for(int i=0; i<s.length(); i++){
char c = s.charAt(i) ;
while(queue.contains(c)){
queue.poll() ;
}
if(!queue.contains(c)){
queue.add(c) ;
}
max = max < queue.size() ? queue.size() : max ;
}
return max ;
}
}
Python版:
class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
max = 0
i = 0
list = []
for s1 in s:
while self.contains_str(s1, list):
list.pop(0)
if not self.contains_str(s1, list):
list.append(s1)
if max < len(list):
max = len(list)
return max
def contains_str(self, s1, list):
for s2 in list:
if s1 == s2:
return True
return False
Js版:
?
/**
* @param {string} s
* @return {number}
*/
var lengthOfLongestSubstring = function(s) {
let max = 0
const arr = []
for(let i=0; i<s.length; i++){
while (arr.includes(s[i])){
arr.shift()
}
if(!arr.includes(s[i])){
arr.push(s[i])
}
max = max < arr.length ? arr.length : max
}
return max
};
思路:题目要求时间复杂度是Olog(m+n),这样的话就不能合并数组后排序了,我们需要找出中位数,但是并不需要合并数组,那么我们要做的就是模拟走(数组1的长度+数组2的长度)/2步即可,每次让两个数组的中小的继续走,若其中一个走完了,则只能走另外一个。最后如果两个数组的长度之和是偶数,则除以2得到中位数,否则直接得到中位数。
Java版:
class Solution {
public double findMedianSortedArrays(int[] nums1, int[] nums2) {
int start1 = 0, start2 = 0 ;
int left = -1, right = -1 ;
int m = nums1.length, n = nums2.length ;
int len = m + n ;
for(int i=0; i<=len/2; i++){
left = right ;
if(start1 < m && (start2 >= n || nums1[start1] <= nums2[start2])){
right = nums1[start1 ++] ;
}else{
right = nums2[start2 ++] ;
}
}
if(len%2 == 0){
return (left + right) / 2.0 ;
}else{
return right ;
}
}
}
Python版:
class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
left = right = -1
start1 = start2 = 0
m = len(nums1)
n = len(nums2)
l = m + n
for i in range(l // 2 + 1):
left = right
# 这里需要注意防止列表下标越界
if (start1 < m) and (start2 >= n or nums1[start1] <= nums2[start2]):
right = nums1[start1]
start1 += 1
else:
right = nums2[start2]
start2 += 1
if l%2 == 0:
return (left + right) / 2
else:
return right
Js版:
/**
* @param {number[]} nums1
* @param {number[]} nums2
* @return {number}
*/
var findMedianSortedArrays = function(nums1, nums2) {
let left = -1, right = -1
let start1 = 0, start2 = 0
const m = nums1.length, n = nums2.length
const len = m+n
for(let i=0; i<=Math.floor(len/2); i++){
left = right
if(start1 < m && (start2 >=n || nums1[start1] <= nums2[start2])){
right = nums1[start1++] ;
}else{
right = nums2[start2++] ;
}
}
if(len % 2 == 0){
return (left + right) / 2 ;
}else{
return right ;
}
};
思路:动态规划,递推式,外层循环很重要,要确保当前所用到的递推式之前推导出了结果。
dp[i][j] = dp[i+1][j-1] s[i] == s[j] && j-i>2
dp[i][j] = true s[i]==s[j] && j-i<=2
dp[i][j] = false? ?s[i] != s[j]
Java版:
class Solution {
public String longestPalindrome(String s) {
int n = s.length() ;
boolean [][] dp = new boolean[n][n] ;
int max = 0 ;
String ans = "" ;
// 动态规划递推式很重要,两层循环,当前所需的表达式要在之前递推出来了才行
for(int j=0; j<n; j++){
for(int i=0; i<=j; i++){
if(s.charAt(i) != s.charAt(j)){
dp[i][j] = false ;
}else{
if(j-i<=2){
dp[i][j] = true ;
}else{
dp[i][j] = dp[i+1][j-1] ;
}
}
if(dp[i][j] && max < j - i + 1){
max = j - i + 1 ;
ans = s.substring(i,j+1) ;
}
}
}
return ans ;
}
}
Python版:
class Solution:
def longestPalindrome(self, s: str) -> str:
row = column = len(s)
matrix = [[False for _ in range(column)] for _ in range(row)]
max = 0
res = ""
for j in range(column):
for i in range(j+1):
if s[i] == s[j]:
if j - i <= 2:
matrix[i][j] = True
else:
matrix[i][j] = matrix[i+1][j-1]
if matrix[i][j] and j-i+1 >= max:
max = j - i + 1
res = s[i:j+1]
return res
Js版:
/**
* @param {string} s
* @return {string}
*/
var longestPalindrome = function(s) {
let max = 0
let ans = ""
const n = s.length
const arr = new Array(n)
for(let i=0; i<n; i++){
arr[i] = new Array(n).fill(false) ;
}
for(let j=0; j<n; j++){
for(let i=0; i<=j; i++){
if(s.charAt(i) == s.charAt(j)){
if(j-i<=2){
arr[i][j] = true
}else{
arr[i][j] = arr[i+1][j-1]
}
}
if(arr[i][j] && max < j - i + 1){
max = j - i + 1
ans = s.substring(i,j+1)
}
}
}
return ans
};
今天的算法题就到这里喽,我们下次继续,道阻且长,行则降至!