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"What are difference between Prim's algorithm and Kruskal's algorithm for finding the minimum spanning tree of a graph?"
Prim's method starts with one vertex of a graph as your tree, and adds the smallest edge that grows your tree by one more vertex. Kruskal starts with all of the vertices of a graph as a forest, and adds the smallest edge that joins two trees in the forest. Prim's method is better when * You can only concentrate on one tree at a time * You can concentrate on only a few edges at a time
Kruskal's method is better when * You can look at all of the edges at once * You can hold all of the vertices at once * You can hold a forest, not just one tree
Basically, Kruskal's method is more time-saving (you can order the edges by weight and burn through them fast), while Prim's method is more space-saving (you only hold one tree, and only look at edges that connect to vertices in your tree).
What else can I help you with?
Advantages of Secure hash algorithm?
because it is more secure than any other algorithm.
Why time complexity is better than actual running time?
Finding a time complexity for an algorithm is better than measuring the actual running time for a few reasons: # Time complexity is unaffected by outside factors; running time is determined as much by other running processes as by algorithm efficiency. # Time complexity describes how an algorithm will scale; running time can only describe how one particular set of inputs will cause the algorithm to perform. Note that there are downsides to time complexity measurements: # Users/clients do not care about how efficient your algorithm is, only how fast it seems to run. # Time complexity is ambiguous; two different O(n2) sort algorithms can have vastly different run times for the same data. # Time complexity ignores any constant-time parts of an algorithm. A O(n) algorithm could, in theory, have a constant ten second section, which isn't normally shown in big-o notation.
What is polynomail time algorithm?
That means, roughly speaking, that for any input of size "x", the algorithm will take no longer than xn for some constant "n".
Why does a multiplication algorithm work the way it does?
The answer to this question depends on the multiplication algorithm you are working with. If you are working with an algorithm for multiplying fractions, the answer of why it works the way it does is going to be different than if you are multiplying whole numbers. If you are looking to explain multiplication algorithms to young children (and even to explain algorithms to older children or to better understand them yourself), it is useful to use physical objects and play with multiplication. Once you work out a few of the type of problem you are doing (or a scaled down version if you are working with large numbers) it will likely become clearer to you why it works the way it does.
What is c plus plus program use to convert algorithm in to c plus plus program?
You can't convert an algorithm into code. That is the job of the programmer, not the language. Algorithm's are expressed in plain-English and typically use pseudocode to broadly demonstrate the implementation of the algorithm. However, it is the programmer's job to convert these algorithms into working code. Pseudocode isn't a programming language as such, but it uses structures and statements that are familiar to any programmer and can be easily translated into any language. However, pseudocode is not a standard so there are many different ways to present pseudocode to the programmer. Moreover, pseudocode is generalised and is far too generic to be converted directly into any one language, never mind C++, which can take advantage of the underlying hardware to produce more efficient algorithms than would otherwise be implied by the pseudocode alone. Hence the need for plain-English algorithms in conjunction with the pseudocode. Programmer's can process all this information far more easily than any computer can. Even if you could program a converter for one algorithm, there's no guarantee it would work for any other algorithm. The time spent programming an algorithm converter would be far better spent simply translating the algorithm yourself.
Is 51 prims or composite?
51 is a composite number because it has more than 2 factors
How does the efficiency of an algorithm in terms of time complexity differ when comparing n log n to n?
When comparing the efficiency of algorithms in terms of time complexity, an algorithm with a time complexity of n log n is generally more efficient than an algorithm with a time complexity of n. This means that as the input size (n) increases, the algorithm with n log n will perform better and faster than the algorithm with n.
What is the algorithm of canny detector?
In case of canny detector, we may say that it is too complex to have its algorithm. It is more than minimax AI algorithm.
Advantages of Secure hash algorithm?
because it is more secure than any other algorithm.
How many more vertices does a triangular prims have than a triangular pyramid?
Triangular prism has 6. Triangular pyramid has 4. Answer: 2 more vertices.
Why time complexity is better than actual running time?
Finding a time complexity for an algorithm is better than measuring the actual running time for a few reasons: # Time complexity is unaffected by outside factors; running time is determined as much by other running processes as by algorithm efficiency. # Time complexity describes how an algorithm will scale; running time can only describe how one particular set of inputs will cause the algorithm to perform. Note that there are downsides to time complexity measurements: # Users/clients do not care about how efficient your algorithm is, only how fast it seems to run. # Time complexity is ambiguous; two different O(n2) sort algorithms can have vastly different run times for the same data. # Time complexity ignores any constant-time parts of an algorithm. A O(n) algorithm could, in theory, have a constant ten second section, which isn't normally shown in big-o notation.
Need of fft?
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
How does the time complexity of an algorithm differ when comparing log(n) versus n?
When comparing the time complexity of an algorithm with log(n) versus n, log(n) grows slower than n. This means that an algorithm with log(n) time complexity will generally be more efficient and faster than an algorithm with n time complexity as the input size increases.
What is polynomail time algorithm?
That means, roughly speaking, that for any input of size "x", the algorithm will take no longer than xn for some constant "n".
How does the time complexity of an algorithm with a runtime of O(log n) compare to that of an algorithm with a runtime of O(n)?
An algorithm with a runtime of O(log n) has a faster time complexity compared to an algorithm with a runtime of O(n). This means that as the input size (n) increases, the algorithm with O(log n) will have a more efficient performance than the one with O(n).
How do you create an algorithm that will read the values of A and B and will determine which has the higher value?
The algorithm can be easily stated as follows: if A is greater than B then return A, otherwise return B.
Give 10 difference between dda and bresenham algorithm?
DDA algorithm involves floating-point operations, while Bresenham algorithm uses only integer operations. DDA algorithm calculates the exact position of each pixel, while Bresenham algorithm determines the closest pixel to the ideal line path. DDA algorithm can suffer from precision issues due to floating-point calculations, while Bresenham algorithm is more accurate and efficient. DDA algorithm is simpler to implement but slower than Bresenham algorithm. DDA algorithm is susceptible to rounding errors, while Bresenham algorithm is not. DDA algorithm can produce jagged lines due to rounding errors, while Bresenham algorithm generates smoother lines. DDA algorithm is suitable for both lines and circles, while Bresenham algorithm is primarily used for drawing lines. DDA algorithm can handle lines with any slope, while Bresenham algorithm is more efficient for lines with slopes close to 0 or 1. DDA algorithm involves multiplication and division operations, while Bresenham algorithm uses addition and subtraction operations. DDA algorithm is a general line drawing algorithm, while Bresenham algorithm is specialized for line drawing and rasterization.
