time-complexity

问题 I am trying to implement the following Bowyer-Watson algorithm to implement Delaunay Triangulation. function BowyerWatson (pointList) // pointList is a set of coordinates defining the points to be triangulated triangulation := empty triangle mesh data structure add super-triangle to triangulation // must be large enough to completely contain all the points in pointList for each point in pointList do // add all the points one at a time to the triangulation badTriangles := empty set for each

问题 I have a program doing a LOT of iteration (thousands to millions to hundreds of millions). It's starting to take quite a lot of time (few minutes, to a few days), and despite all my effort to optimize it, I'm still a bit stuck. Profile: using cProfile via console call ncalls tottime percall cumtime percall filename:lineno(function) 500/1 0.018 0.000 119.860 119.860 {built-in method builtins.exec} 1 0.006 0.006 119.860 119.860 Simulations_profiling.py:6(<module>) 6/3 0.802 0.134 108.302 36.101

问题 I thought that, when proving that a problem P is NP-Complete, we were supposed to reduce a known NPC problem to P. But, looking at the solution to the Independent Set problem, it seems to not go this way. To prove that Independent Set is NP-Complete, you take a graph G, find its inverse G', and then compute CLIQUE(G'). But, this is doing the other way around: it's taking a problem P I DON'T know if it's NPC and then reduces it to a know NPC problem. Here's an example of the solution. What am

问题 How does one find the time complexity of a given algorithm notated both in N and Big-O? For example, //One iteration of the parameter - n is the basic variable void setUpperTriangular (int intMatrix[0,…,n-1][0,…,n-1]) { for (int i=1; i<n; i++) { //Time Complexity {1 + (n+1) + n} = {2n + 2} for (int j=0; j<i; j++) { //Time Complexity {1 + (n+1) + n} = {2n + 2} intMatrix[i][j] = 0; //Time complexity {n} } } //Combining both, it would be {2n + 2} * {2n + 2} = 4n^2 + 4n + 4 TC } //O(n^2) Is the

问题 I wrote couple sorting algorithm. I want to compare their sorting times, at least nearly. But after first loop all sorting times decreasing except StoogeSort. I think something optimizating at background but which measure should I consider? First one or the others? And why is this happening? public static void main(String[] args) { RandomNumber rn = new RandomNumber(); Scanner sc = new Scanner(System.in); while(true){ System.out.println("Enter the input size."); int n = sc.nextInt(); int[]

问题 I've developed an application to encrypt / decrypt a file. The user can choose from 3 different algorithms i.e. AES or Blowfish or PBE. And then the encryption, decryption and total time would be displayed. I'm trying to compare the efficiency of the 3 algos w.r.t the element of "time". My professor at college has told me to study the time complexity of these 3 algorithms. What are the other ways to determine algorithm efficiency w.r.t speed in addition to calculating the time complexity? Is

问题 I am looking into the time complexities of Machine Learning Algorithms and I cannot find what is the time complexity of Logistic Regression for predicting a new input. I have read that for Classification is O(c*d) c-beeing the number of classes, d-beeing the number of dimensions and I know that for the Linear Regression the search/prediction time complexity is O(d). Could you maybe explain what is the search/predict time complexity of Logistic Regression? Thank you in advance Example For The

问题 For example, if the input string is “ABC”, then output should be “ABC, ACB, BAC, BCA, CAB, CBA”. Here is my approach : #include<stdio.h> #include<conio.h> #include<string.h> void print(char str[],int visited[],int index,char temp[],int len) { if(index==len) { temp[index]='\0'; printf("\n%s",temp); return; } for(int i=0;i<len;i++) { if(!visited[str[i]-'A']) { visited[str[i]-'A']=1; temp[index]=str[i]; print(str,visited,index+1,temp,len); visited[str[i]-'A']=0; } } } int main() { int visited[20

问题 How is the bottom up approach of heap construction of the order O(n) ? Anany Levitin says in his book that this is more efficient compared to top down approach which is of order O(log n). Why? 回答1: That to me seems like a typo. There are two standard algorithms for building a heap. The first is to start with an empty heap and to repeatedly insert elements into it one at a time. Each individual insertion takes time O(log n), so we can upper-bound the cost of this style of heap-building at O(n

问题 We all know that merge sorting algorithm time complexity is "N Log N". But from this below code how to calculate this "N Log N" big O notation step by step ? There are some answer about this question on internet but they are very complicated to understand. Please help me to understand. Million thanks in advance. #include<stdio.h> #define MAX 50 void mergeSort(int arr[],int low,int mid,int high); void partition(int arr[],int low,int high); int main(){ int merge[MAX],i,n; printf("Enter the