Answer:
78.5 in ^2
Step-by-step explanation:
radius = 5
A = 25π
= 78.5
Answer:
3
Step-by-step explanation:
equation, y=3x, because when x is 1, y is 3, so the equation is y=3x, and the slope is equal to how much of x is equal to y, so 3
Pls brainliest
X to the 8/15 power. When you have a power raised to another power, you multiply the exponents.
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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You can pick any of them it won’t hold it against you.
I do it all the time.
Have a good day