Table of Contents
Which of the following data structure is used to store sparse data?
Discussion Forum
| Que. | To implement Sparse matrix dynamically, the following data structure is used |
|---|---|
| b. | Graphs |
| c. | Priority Queues |
| d. | Linked List |
| Answer:Linked List |
Why matrix multiplication is faster?
As matrices grow larger, the number of multiplications needed to find their product increases much faster than the number of additions.
Why is Stressen’s matrix multiplication better?
It just gives the sequence in which a chain of matrices to be multiplied so that number of multiplications between matrix elements are minimum. You are probably asking how Strassen’s is better than Native matrix multiplication. This is because number of multiplications required in strassen’s is less.
Which of the following is sparse matrix?
Which one of the following is a Special Sparse Matrix? Explanation: A band matrix is a sparse matrix whose non zero elements are bounded to a diagonal band, comprising the main diagonal and zero or more diagonals on either side. 9.
How long does matrix multiplication take?
Directly applying the mathematical definition of matrix multiplication gives an algorithm that takes time on the order of n3 field operations to multiply two n × n matrices over that field (Θ(n3) in big O notation).
Is Numpy Matmul fast?
Tensorflow matmul We can directly pass the numpy arrays without having to convert to tensorflow tensors but it performs a bit slower. In my experiments, if I just call py_matmul5(a, b) , it takes about 10 ms but converting numpy array to tf. Tensor using tf. constant function yielded in a much better performance.
What is a sparse matrix data structure?
Sparse matrix data structures IOnly nonzero elements are stored in sparse matrix data structures, which makes possible the storage of sparse matrices of large dimension. ISometimes some zeros are stored (explicit zeros) to maintain block or symmetric sparsity patterns, for example.
How do you do sparse matrix-vector multiplication?
All sparse matrix-vector multiplication algorithms that I have ever seen boil down to the same steps. For example consider y = Ax. Zeroise the result vector, y. Get the next non-zero element of the matrix, A [i,j] say.
What is the space complexity of a matrix with 3 columns?
We can see that there are fixed 3 columns, the number of rows varies with the number of non-zero elements. So if there are T number of non-zero elements, then the space Complexity will be O (3*T) = O (T). For the matrix it will be O (m x n).
What is compressed sparse row format (CSR)?
Compressed sparse row format (CSR) Example: 2 4 10 11 12 13 14 3 5 CSR format uses three arrays for the above matrix: with N=3. Rows are stored contiguously in memory. This is useful if row-wise access should be effcient.