Prim's Algorithm
Definition:
Prim's algorithm is a greedy algorithm that finds the minimum spanning tree of a largely undirected graph. This means that the edges that make up the tree containing each sentence find their edges and the total weight of all the edges in the tree is reduced.
How to solve Prim's Algorithm?If you want to see the solving video on YouTube in Hindi/Urdu Language. Click here
Description:
The algorithm can be defined informally by following these steps:
Initialize a random unigram tree from the graph.
Edge Growing Tree: Find the edge where the heaviest edge connects the tree to the node in the tree and attach it to the tree.
Repeat step 2 (until all nodes are in the tree).
can be done in more detail below as pseudocode.
associates each vertex of the graph with the number (the cheapest connection to v) and the edge (the edge with the cheapest connection). To initialize the value, set all values of to + (or any number greater than the maximum) and set each vertex to the value of the custom flag that indicates no previously connected edge. V. vertical
Initialize empty forest F and parameter set in F (all initial vertices).
Repeat these steps until Q is empty: Find and eliminate the expression that is the minimum of C for
Q. Add f to
v loop between edges connecting
W to other vertices. For each of these edges, if w is still Q and vv is less than do the following:
Set the edge of to the value of vv
, set to specify the edge. Back to F
with the corresponding edge in Section
E As mentioned above, the first vertices of the algorithm will be arbitrarily chosen, since there will be a number of vertices in Q in the first iteration of the main loop of the algorithm. have the same weight and the algorithm will automatically start a new tree. When F completes the spanning tree for all links in the chart.
The algorithm can be modified to start with all head s by making less than any other value of C (like zero) and to find only one tree. The entire spanning forest (various explanations) stops when it encounters another line whose edge is marked as absent.
Different versions of the
algorithm differ in how Q sets are used: as simple linked lists or vertical arrays, or as the most important datasets. This option affects the time complexity of the algorithm. In general, the row's values will find the v minimum faster, but will need to be updated more expensively if the value changes.
Description:
The algorithm can be defined informally by following these steps:
Initialize a random unigram tree from the graph.
Edge Growing Tree: Find the edge where the heaviest edge connects the tree to the node in the tree and attach it to the tree.
Repeat step 2 (until all nodes are in the tree).
can be done in more detail below as pseudocode.
associates each vertex of the graph with the number (the cheapest connection to v) and the edge (the edge with the cheapest connection). To initialize the value, set all values of to + (or any number greater than the maximum) and set each vertex to the value of the custom flag that indicates no previously connected edge. V. vertical
Initialize empty forest F and parameter set in F (all initial vertices).
Repeat these steps until Q is empty: Find and eliminate the expression that is the minimum of C for
Q. Add f to
v loop between edges connecting
W to other vertices. For each of these edges, if w is still Q and vv is less than do the following:
Set the edge of to the value of vv
, set to specify the edge. Back to F
with the corresponding edge in Section
E As mentioned above, the first vertices of the algorithm will be arbitrarily chosen, since there will be a number of vertices in Q in the first iteration of the main loop of the algorithm. have the same weight and the algorithm will automatically start a new tree. When F completes the spanning tree for all links in the chart.
The algorithm can be modified to start with all head s by making less than any other value of C (like zero) and to find only one tree. The entire spanning forest (various explanations) stops when it encounters another line whose edge is marked as absent.
Different versions of the
algorithm differ in how Q sets are used: as simple linked lists or vertical arrays, or as the most important datasets. This option affects the time complexity of the algorithm. In general, the row's values will find the v minimum faster, but will need to be updated more expensively if the value changes.
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