In linear algebra, the rank of a matrix
A
A is the dimension of the vector space generated (or spanned) by its columns.[1] This corresponds to the maximal number of linearly independent columns of
A
A. This, in turn, is identical to the dimension of the vector space spanned by its rows.[2] Rank is thus a measure of the "nondegenerateness" of the system of linear equations and linear transformation encoded by
A
A. There are multiple equivalent definitions of rank. A matrix's rank is one of its most fundamental characteristics.
The rank is commonly denoted by
rank
(
A
)
{\displaystyle \operatorname {rank} (A)} or
rk
(
A
)
{\displaystyle \operatorname {rk} (A)}; sometimes the parentheses are not written, as in
rank
A
{\displaystyle \operatorname {rank} A}.
Answer:
Step-by-step explanation:
<u>Missing value looks the same, but it's 0.7 ft. longer than 2 ft.</u>
<u>Your answer is 2.7 (C).</u>
Answer:
308[cos(45) + isin(45)]
Step-by-step explanation:
z1×z2:
Modulus: r1 × r2
= 7×44 = 308
Argument: theta1 + theta2
= -70 + 115 = 45
z1z2 = 308[cos(45) + isin(45)]
Or
z1z2 = 154sqrt(2) + (i)154sqrt(2)
sqrt: square root
Step-by-step explanation:
If a variables varies jointly, we can just divide it by the other variables in relation to it.
For example, since p variables jointly as q and square of r, then
where k is a constant
First, let find k. Substitute p= 200
q= 2, and r=3.
Now, since we know our constant, let find p.
Q is 5, and r is 2.
