我在一次面试中被问到这个问题。它们都是O(nlogn),但大多数人使用快速排序而不是归并排序。为什么呢?
当前回答
虽然它们都在相同的复杂度类中,但这并不意味着它们都具有相同的运行时。快速排序通常比归并排序更快,因为它更容易编写紧凑的实现代码,它所做的操作也更快。这是因为快速排序通常更快,人们使用它而不是归并排序。
然而!我个人经常会使用归并排序或快速排序变体,当快速排序表现不佳时,它们会降级为归并排序。记住。快速排序平均只有O(n log n)最坏情况是O(n²)归并排序总是O(n log n).在实时性能或响应性是必须的情况下,你的输入数据可能来自恶意来源,你不应该使用简单的快速排序。
其他回答
这是采访中经常被问到的一个问题,尽管归并排序在最坏情况下性能更好,但快速排序被认为比归并排序更好,特别是对于大输入。以下是快速排序更好的原因:
1-辅助空间:快速排序是一种就地排序算法。就地排序意味着执行排序不需要额外的存储空间。另一方面,归并排序需要一个临时数组来归并已排序的数组,因此它并不到位。
2-最坏情况:快速排序O(n^2)的最坏情况可以通过使用随机化快速排序来避免。通过选择正确的枢轴,可以很容易地避免这种情况。通过选择合适的枢轴元来获得平均情况下的行为,从而提高了算法的性能,达到了与归并排序一样的效率。
3-引用的局部性:快速排序特别展示了良好的缓存局部性,这使得它在许多情况下比归并排序更快,比如在虚拟内存环境中。
4-尾递归:快速排序是尾递归,而归并排序不是。尾递归函数是一种函数,其中递归调用是函数执行的最后一件事。尾递归函数被认为比非尾递归函数更好,因为尾递归可以被编译器优化。
One of the reason is more philosophical. Quicksort is Top->Down philosophy. With n elements to sort, there are n! possibilities. With 2 partitions of m & n-m which are mutually exclusive, the number of possibilities go down in several orders of magnitude. m! * (n-m)! is smaller by several orders than n! alone. imagine 5! vs 3! *2!. 5! has 10 times more possibilities than 2 partitions of 2 & 3 each . and extrapolate to 1 million factorial vs 900K!*100K! vs. So instead of worrying about establishing any order within a range or a partition,just establish order at a broader level in partitions and reduce the possibilities within a partition. Any order established earlier within a range will be disturbed later if the partitions themselves are not mutually exclusive.
任何自下而上的排序方法,如归并排序或堆排序,就像工人或雇员的方法一样,人们很早就开始在微观层面进行比较。但是,一旦在它们之间发现了一个元素,这个顺序就必然会丢失。这些方法非常稳定和可预测,但要做一定量的额外工作。
Quick Sort is like Managerial approach where one is not initially concerned about any order , only about meeting a broad criterion with No regard for order. Then the partitions are narrowed until you get a sorted set. The real challenge in Quicksort is in finding a partition or criterion in the dark when you know nothing about the elements to sort. That is why we either need to spend some effort to find a median value or pick 1 at random or some arbitrary "Managerial" approach . To find a perfect median can take significant amount of effort and leads to a stupid bottom up approach again. So Quicksort says just a pick a random pivot and hope that it will be somewhere in the middle or do some work to find median of 3 , 5 or something more to find a better median but do not plan to be perfect & don't waste any time in initially ordering. That seems to do well if you are lucky or sometimes degrades to n^2 when you don't get a median but just take a chance. Any way data is random. right. So I agree more with the top ->down logical approach of quicksort & it turns out that the chance it takes about pivot selection & comparisons that it saves earlier seems to work better more times than any meticulous & thorough stable bottom ->up approach like merge sort. But
正如其他人所注意到的,快速排序的最坏情况是O(n²),而归并排序和堆排序则停留在O(nlogn)。然而,在平均情况下,这三个都是O(nlogn);所以它们在大多数情况下是可比较的。
平均而言,快速排序更好的地方在于,内循环意味着将多个值与单个值进行比较,而在其他两个循环中,每次比较时两个项都是不同的。换句话说,Quicksort的读取次数是其他两种算法的一半。在现代cpu上,访问时间在很大程度上决定了性能,因此快速排序最终成为一个很好的首选。
快速排序有O(n2)最差情况运行时和O(nlogn)平均情况运行时。然而,在许多情况下,它优于归并排序,因为许多因素影响算法的运行时,并且,当把它们放在一起时,快速排序胜出。
In particular, the often-quoted runtime of sorting algorithms refers to the number of comparisons or the number of swaps necessary to perform to sort the data. This is indeed a good measure of performance, especially since it’s independent of the underlying hardware design. However, other things – such as locality of reference (i.e. do we read lots of elements which are probably in cache?) – also play an important role on current hardware. Quicksort in particular requires little additional space and exhibits good cache locality, and this makes it faster than merge sort in many cases.
此外,通过使用适当的枢轴选择,几乎完全避免快速排序的最坏情况运行时间O(n2)是非常容易的——比如随机选择它(这是一个很好的策略)。
在实践中,许多现代的快速排序实现(特别是libstdc++的std::sort)实际上是introsort,其理论上的最差情况是O(nlogn),与归并排序相同。它通过限制递归深度,并在超过logn时切换到不同的算法(heapsort)来实现这一点。
实际上,快速排序是O(n2)。它的平均情况运行时间是O(nlog(n)),但最坏情况是O(n2),这发生在在包含很少唯一项的列表上运行时。随机化花费O(n)。当然,这并没有改变最坏的情况,它只是防止恶意用户使您的排序花费很长时间。
快速排序更受欢迎,因为它:
(MergeSort需要额外的内存,与要排序的元素数量成线性关系)。 有一个小的隐藏常数。