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Example Questions
Example Question #122 : Matrices
Let be a matrix and be a vector defined by
Find the product .
The product does not exist because the dimensions do not match.
The product does not exist because the dimensions do not match.
The matrix has 3 columns and the vector has 5 rows. The dimensions do not match and the product does not exist.
Example Question #123 : Matrices
Rewrite the system of equations:
into a matrix vector product:
where is a 3x3 matrix and are vectors in .
To write
into matrix vector form, we recall that matrix multiplication with a vector is done such that the first element in the resulting vector is the dot product of the first row of with the vector , the second element is the dot product of the second row with , and so on. The first row is thus , the second row is , and the third row is . So the left side of the equality is
The right side is the vector , so the final answer is
which is equivalent to
Example Question #811 : Linear Algebra
Let and .
Find .
is not defined.
First, it must be established that is defined. This is the case if and only if has as many columns as has rows. Since has two columns and has two rows, is defined.
Matrices are multiplied by multiplying each row of the first matrix by each column of the second - that is, by adding the products of the entries in corresponding positions. Thus,
Example Question #21 : Matrix Vector Product
Multiply
To multiply, add:
Example Question #1 : Vector Vector Product
Compute , where
Not possible
Before we compute the product of , and , we need to check if it is possible to take the product. We will check the dimensions. is , and is , so the dimensions of the resulting matrix will be . Now let's compute it.
Example Question #121 : Matrices
Find the vector-vector product of the following vectors.
It's not possible to multiply these vectors
Example Question #1 : Vector Vector Product
Calculate , given
By definition,
.
Example Question #3 : Vector Vector Product
What is the physical significance of the resultant vector , if ?
is orthogonal to both and .
is a scalar.
lies in the same plane that contains both and .
is the projection of onto .
is orthogonal to both and .
By definition, the resultant cross product vector (in this case, ) is orthogonal to the original vectors that were crossed (in this case, and ). In , this means that is a vector that is normal to the plane containing and .
Example Question #4 : Vector Vector Product
Example Question #4 : Vector Vector Product
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