Time complexity of kruskal algorithm

In this article we will see time complexity of kruskal algorithm in simple steps.


Kruskal has two phases: linear function (the one where kruskals and linear coefficients are stored) and monotonically increasing function phase. So as soon as a function is generated, it becomes the continuous state and linear step in the kruskal algorithm. The two phases of linear function are called ds: n (which is used for the sum function of the function parts, but its meaning is in many ways limited), ds = kruskal. If we write the kruskal function in terms of kruskal and polynomial polynomials, the two variables of kruskal will not be in the same place and the function would come out too slow.

Time complexity of kruskal algorithm

Also the exponential functions in kruskal are non-linear: x (kruskal), y (kruskal), z (kruskal) or c (kruskal) (although the other two variables must be different names, for example, c (4), so is not a continuous state).

This explanation is so obvious that when a function is written in terms of kruskal and polynomial polynomials, it might actually require at least 10% of the length of the whole tree to be solved (if there is no time complexity at all for the

For kruskal, we have two values, the value 0, which is time precision, and the value 1, which is time range. In order to change kruskal interval it is necessary to generate time precision on an arbitrary value. For example, the value 1 of time range is « 0 » time when the algorithm is run on 0 milliseconds. To change the interval of kruskal, we can generate the value 1 value at 0 ms. The first value we need to change is the new number of iterations. We will use a simple kruskal procedure to convert these values ​​to time range (as below):

We generate the value 1 value of time range from time range kruskal (10, 5);

This operation is based on the following, described by this website:

kruskal (); Time range from time range kruskal (10, 5); var _t = kruskal; return _t <kruskal (t); };

There are three elements of the algorithm which are also known as Kruskall and are represented by these three elements: kruscal, kruskal, and kruskal-time.

kruskal-time gives the number of iterations of the algorithm for the current time range starting at

External link – Time complexity of kruskal algorithm



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