time-complexity

问题 I am trying to find the Time Complexity of this algorithm. The iterative: algorithm produces all the bit-strings within a given Hamming distance, from the input bit-string. It generates all increasing sequences 0 <= a[0] < ... < a[dist-1] < strlen(num) , and reverts bits at corresponding indices. The vector a is supposed to keep indices for which bits have to be inverted. So if a contains the current index i , we print 1 instead of 0 and vice versa. Otherwise we print the bit as is (see else

问题 I am trying to find the Time Complexity of this algorithm. The iterative: algorithm produces all the bit-strings within a given Hamming distance, from the input bit-string. It generates all increasing sequences 0 <= a[0] < ... < a[dist-1] < strlen(num) , and reverts bits at corresponding indices. The vector a is supposed to keep indices for which bits have to be inverted. So if a contains the current index i , we print 1 instead of 0 and vice versa. Otherwise we print the bit as is (see else

问题 what is the time complexity of javascript's array .length? I think it would be constant since it seems that property is set automatically on all arrays and you're just looking it up? 回答1: I think it would be constant since it seems that property is set automatically on all arrays and you're just looking it up? Right. Probably . It's a property which is stored (not calculated) and automatically updated as necessary. Having said that, remember that JavaScript engines are free to do what they

问题 So I am following this algorithm for Prim's MST input: graph G(V,E) in the form of adjacency list Create a min heap for vertices using build heap time complexity: O(V) Repeat following steps until no more elements in heap. Call extract-min on heap to get vertex with minimum cost O(log V) For vertex extracted find the adjacent vertices in adjacency list. Perform decrease key operation on adjacent vertices in the heap. O(logn V) The time complexity analysis for the algorithm given in my class

问题 So I am following this algorithm for Prim's MST input: graph G(V,E) in the form of adjacency list Create a min heap for vertices using build heap time complexity: O(V) Repeat following steps until no more elements in heap. Call extract-min on heap to get vertex with minimum cost O(log V) For vertex extracted find the adjacent vertices in adjacency list. Perform decrease key operation on adjacent vertices in the heap. O(logn V) The time complexity analysis for the algorithm given in my class

问题 I'm looking for the mathematical proof, not just the answer. 回答1: The recurrence relation of binary search is (in the worst case) T(n) = T(n/2) + O(1) Using Master's theorem n is the size of the problem. a is the number of subproblems in the recursion. n/b is the size of each subproblem. (Here it is assumed that all subproblems are essentially the same size.) f (n) is the cost of the work done outside the recursive calls, which includes the cost of dividing the problem and the cost of merging

问题 I have a question regarding time complexity (big O notation) for Java software. Is there a way to quickly calculate or test it (or any website that could calculate it for me would be welcomed). For example I would like to check it for the following snippet of code and possibly improve as well: int dcount = 24423567; int a = 0; if (dcount == 0){ a = 1; } String ds = Integer.toString(dcount); String[] sa = ds.split("(?<=.)"); HashSet hs = new HashSet(); Collections.addAll(hs, sa); a = hs.size()

问题 I have written the following class: public class SortingObjectsWithAngleField implements Comparator<Point> { public int compare(Point p1, Point p2) { double delta = p1.getAngle() - p2.getAngle(); if(delta == 0.00001) return 0; return (delta > 0.00001) ? 1 : -1; } } Then, in my main() method, I have created a List to which I add some objects which has "X" and "angle" field. I then use: Collections.sort(list, new SortingObjectsWithAngleField()); What is the complexity of this sort method? 回答1:

问题 I'm currently taking an algorithm class, and we're covering Big O notations and such. Last time, we talked about how O (n^2 + 3n + 5) = O(n^2) And I was wondering, if the same rules apply to this: O(n^2) + O(3n) + O(5) = O(n^2) Also, do the following notations hold ? O(n^2) + n or O(n^2) + Θ (3n+5) The later n is outside of O, so I'm not sure what it should mean. And in the second notation, I'm adding O and Θ . 回答1: At least for practical purposes, the Landau O(...) can be viewed as a

问题 Interview questions where I start with "this might be solved by generating all possible combinations for the array elements" are usually meant to let me find something better. Anyway I would like to add "I would definitely prefer another solution since this is O(X)".. the question is: what is the O(X) complexity of generating all combinations for a given set? I know that there are n! / (n-k)!k! combinations (binomial coefficients), but how to get the big-O notation from that? 回答1: First,