更快的方法-将2个排序后的数组合并为一个没有重复值的排序后的数组 [英] What is faster - merge 2 sorted arrays into a sorted array w/o duplicate values

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问题描述

我试图找出执行以下任务的最快方法:



编写一个函数,该函数接受两个数组作为参数-每个数组都是一个排序后的严格递增的整数数组,并返回一个新的严格递增的整数数组,其中包含两个输入数组中的所有值。



例如。合并[1、2、3、5、7]和[1、4、6、7、8]应该返回[1、2、3、4、5、6、7、8]。



由于我没有接受过正规的编程教育,所以我觉得算法和复杂性有点陌生:)我提供了两种解决方案,但是我不确定哪一种更快。 p>

解决方案1(它实际上使用第一个数组而不是创建一个新数组):

  function mergeSortedArrays(a,b){
for(let i = 0,bLen = b.length; i< bLen; i ++){
if(!a.includes(b [ i])){
a.push(b [i])
}
}
console.log(a.sort());
}

const foo = [1、2、3、5、7],
bar = [1、4、6、7、8];
mergeSortedArrays(foo,bar);

和解决方案2:

  function mergeSortedArrays(a,b){
let mergedAndSorted = [];

while(a.length || b.length){
if(typeof a [0] ==='undefined'){
mergedAndSorted.push(b [0 ]);
b.splice(0,1);
}否则,如果(a [0]> b [0]){
mergedAndSorted.push(b [0]);
b.splice(0,1);
} else if(a [0]< b [0]){
mergedAndSorted.push(a [0]);
a.splice(0,1);
} else {
mergedAndSorted.push(a [0]);
a.splice(0,1);
b.splice(0,1);
}
}
console.log(mergedAndSorted);
}

const foo = [1、2、3、5、7],
bar = [1、4、6、7、8];
mergeSortedArrays(foo,bar);

有人可以帮我解决这两种解决方案的时间复杂性吗?另外,还有另一种更快的解决方案吗?我应该使用reduce()吗?

解决方案

复杂度还取决于您在代码中使用的方法。



1)排序中常用的算法是Quicksort(作为快速排序的变体,是introsort)。



它具有O( n log n)复杂度,但是如果已经对输入进行排序,最坏的情况可能仍然是O(n ^ 2)。因此,您的解决方案具有O(length(b)+ length(a)+ length(b)log(length(a)+ length(b)))运行时复杂度。



2)根据 splice()的复杂性上的此问题,最糟糕的javascript函数需要O(n)个步骤(将所有元素复制到大小为n + 1的新数组中)。因此,您的第二个解决方案将数组b的长度乘以在剪接期间复制元素所需的n个步骤以及push()的时间。



对于在线性时间O(n + m)中有效的良好解决方案,请参考此 Java示例并将其移植(即创建长度为(a)+长度(b)的数组,并通过索引逐步显示)。或在答案下方查看非常紧密甚至更快的实现。


I was trying to figure out which is the fastest way to do the following task:

Write a function that accepts two arrays as arguments - each of which is a sorted, strictly ascending array of integers, and returns a new strictly ascending array of integers which contains all values from both of the input arrays.

Eg. merging [1, 2, 3, 5, 7] and [1, 4, 6, 7, 8] should return [1, 2, 3, 4, 5, 6, 7, 8].

Since I don't have formal programming education, I fint algorithms and complexity a bit alien :) I've came with 2 solutions, but I'm not sure which one is faster.

solution 1 (it actually uses the first array instead of making a new one):

function mergeSortedArrays(a, b) {
  for (let i = 0, bLen = b.length; i < bLen; i++) {
    if (!a.includes(b[i])) {
      a.push(b[i])
    } 
  }
  console.log(a.sort());
}

const foo = [1, 2, 3, 5, 7],
      bar = [1, 4, 6, 7, 8];
mergeSortedArrays(foo, bar);

and solution 2:

function mergeSortedArrays(a, b) {
  let mergedAndSorted = [];

  while(a.length || b.length) {
    if (typeof a[0] === 'undefined') {
      mergedAndSorted.push(b[0]);
      b.splice(0,1);
    } else if (a[0] > b[0]) {
      mergedAndSorted.push(b[0]);
      b.splice(0,1);
    } else if (a[0] < b[0]) {
      mergedAndSorted.push(a[0]);
      a.splice(0,1);
    } else {
      mergedAndSorted.push(a[0]);
      a.splice(0,1);
      b.splice(0,1);
    }
  }
  console.log(mergedAndSorted);
}

const foo = [1, 2, 3, 5, 7],
      bar = [1, 4, 6, 7, 8];
mergeSortedArrays(foo, bar);

Can someone help me with time complexity of both solutions? Also, is there another, faster solution? Should I be using reduce()?

解决方案

The complexity also depends on the methods that you use in your code.

1) An algorithm commonly used for sorting is Quicksort (introsort as a variation of quicksort).

It has O(n log n) complexity however the worst case may still be O(n^2) in case the input is already sorted. So your solution has O( length(b) + length(a)+length(b) log (length(a)+length(b)) ) runtime complexity.

2) According to this question on the complexity of splice() the javascript function needs O(n) steps at worst (copying all elements to the new array of size n+1). So your second solution takes length of array b multiplied by n steps needed to copy the elements during splice plus the time to push().

For a good solution that works in linear time O(n+m) refer to this Java example and port it (i.e. create an array of size length(a) + length(b) and step via the indeces as shown). Or check out the very tight and even a littler faster implementation below of the answer.

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