Answer:
120 cm squared
Step-by-step explanation:
Answer:
For Company A to have a better deal, the truck must be driven more than 250 miles per day.
Step-by-step explanation:
Given that:
Rent per day of company A = $70
Per mile charges = $0.20
Let,
x be the number of miles.
A(x) = 0.20x + 70
Rent per day of company B = $20
Per mile charges = $0.40
B(x) = 0.40x + 20
For make Company A better deal,
A(x) > B(x)
0.20x+70 > 0.40+20
0.20x-0.40x>20-70
-0.20x>-50
Dividing both sides by -0.20
Hence,
For Company A to have a better deal, the truck must be driven more than 250 miles per day.
The purpose of the tensor-on-tensor regression, which we examine, is to relate tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without being aware of its intrinsic rank beforehand.
By examining the impact of rank over-parameterization, we suggest the Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN) methods to address the problem of unknown rank. By demonstrating that RGD and RGN, respectively, converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized scenarios, we offer the first convergence guarantee for the generic tensor-on-tensor regression. According to our theory, Riemannian optimization techniques automatically adjust to over-parameterization without requiring implementation changes.
Learn more about tensor-on-tensor here
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For this we will use equation:
H = starting height + rate_of_growth*periods
We can mark H with index w to represent how many weeks have passed.
Because all of this we can write:
Hw = 200 + 0.5*w
To calculate height after some number of weeks all you need to do is to exchange w with number of weeks.
