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What is the difference between tensors and matrices?
A scalar, which is a tensor of rank 0, is just a number, e.g. 6 A vector, which is a tensor of rank 1, is a group of scalars, e.g. [1, 6, 3] A matrix, which is a tensor of rank 2, is a group of vectors, e.g. 1 6 3 9 4 2 0 1 3 A tensor of rank 3 would be a group of matrix and would look like a 3d matrix. A tensor is the general term for all of these, and the generalization into high dimensions.
How do you find rank of a matrix?
First, You have to reduce the matrix to echelon form . The number of nonzero rows in the reduced echelon form matrix (number of linearly independent rows) indicates the rank of the matrix. Go to any search engine and type "Rank of a matrix, Cliffnotes" for an example.
What is the application for rank of the matrix?
Rank of a matrix is used to find consistency of linear system of equations.As we know most of the engineering problems land up with the problem of finding solution of a linear system of equations ,at that point rank of matrix is useful.
How do you generate a parity check matrix?
In order to generate the parity check matrix you must first have the generator matrix and the codeword to check and see if it is correct. 1. Place your generator in row reduction form 2. Get the basis vectors 3. Put the vectors together to get the parity check matrix 4. Check it b multiplying the codewords by the parity = 0 For an example: 2*4 Generator Matrix [1 0 1 1 0 1 1 0] Rank = 2...therefore the number of columns is 2...Rank + X = # of columns of the Generator matrix v1+v3+v4 = 0 v2+v3 = 0 v1 = -r1-r2 v2 = -r1 v3 = r1 v4 = r2 Parity = [-1 -1 -1 0 1 0 0 1]
If rank of the matrix is equal to no of variable then it have?
Then it has (not have!) a unique solution.
The rank of product of two matrices cannot exceed the rank of either factor?
The statement that the rank of product of two matrices cannot exceed the rank of either factor is a true statement. The rank of a matrix is the largest number of linearly independent rows or columns. The column rank is equal to the row rank in every matrix.
What is the difference between tensors and matrices?
A scalar, which is a tensor of rank 0, is just a number, e.g. 6 A vector, which is a tensor of rank 1, is a group of scalars, e.g. [1, 6, 3] A matrix, which is a tensor of rank 2, is a group of vectors, e.g. 1 6 3 9 4 2 0 1 3 A tensor of rank 3 would be a group of matrix and would look like a 3d matrix. A tensor is the general term for all of these, and the generalization into high dimensions.
How do you find rank of a matrix?
First, You have to reduce the matrix to echelon form . The number of nonzero rows in the reduced echelon form matrix (number of linearly independent rows) indicates the rank of the matrix. Go to any search engine and type "Rank of a matrix, Cliffnotes" for an example.
Does the squaring of matrix change its rank?
No, it does not.
What is the application for rank of the matrix?
Rank of a matrix is used to find consistency of linear system of equations.As we know most of the engineering problems land up with the problem of finding solution of a linear system of equations ,at that point rank of matrix is useful.
How do you generate a parity check matrix?
In order to generate the parity check matrix you must first have the generator matrix and the codeword to check and see if it is correct. 1. Place your generator in row reduction form 2. Get the basis vectors 3. Put the vectors together to get the parity check matrix 4. Check it b multiplying the codewords by the parity = 0 For an example: 2*4 Generator Matrix [1 0 1 1 0 1 1 0] Rank = 2...therefore the number of columns is 2...Rank + X = # of columns of the Generator matrix v1+v3+v4 = 0 v2+v3 = 0 v1 = -r1-r2 v2 = -r1 v3 = r1 v4 = r2 Parity = [-1 -1 -1 0 1 0 0 1]
If rank of the matrix is equal to no of variable then it have?
Then it has (not have!) a unique solution.
Why do square matrices only have multiplicative inverses?
there are pseudo inverses for non-square matrices a square matrix has an inverse only if the original matrix has full rank which implies that no vector is annihilated by the matrix as a multiplicative operator
Examples of nullity of a matrix?
A null matrix is a matrix with all its elements zero.EXAMPLES : (0 0) is a null row matrix.(0 0)(0 0) is a null square matrix.NOTE : Text handling limitations prevent the printing of large brackets to enclose the matrix array. Two pairs of smaller brackets have therefore been used.Answer 2:The above answer is a null matrix. However, the nullity of a matrix is the dimension of the kernel. Rank + Nullity = Dimension. So if you have a 4x4 matrix with rank of 2, the nullity must be 2. This nullity is the number of "free variables" you have. A 4x4 matrix is 4 simultaneous equations. If it is rank 2, you have only two independent equations and the other two are dependent. To solve a system of equations, you must have n independent equations for n variables. So the nullity tells you how short you are in terms of equations.
What are the characteristics of geographers?
usually quite high on the rank to dank scale
Does the ring of full rank square matrices form a domain?
It would form a domain, except that it fails to even be a ring. The 0 matrix has rank 0, so it is never a full rank matrix - therefore the set of full rank square matrices doesn't have an additive identity. It is true that there are no zero divisors among the full rank square matrices: if AB=0, and A has full rank, then it's invertible, so A-1AB=A-10, or B=0. Similarly, if BA=0, BAA-1=0A-1 so B=0.
What is a synonym for row?
series, succession, rank, tier, order, line, chain, column, sequence
