Answer:
rate of change = -32 feet/second
Step-by-step explanation:
h(t) = 64 – 16t2
t = time (seconds)
Look at the start of time where t = 0
h(t) = 64 - 16 * (0) * 2 = 64 feet
Then look at the specified time where t = 1.25 seconds
h(t) = 64 - 16 * (1.25sec) * 2 = 24 feet
rate of change = rate of change y / rate of change x
x = time, y = height
rate of change = (24 - 64) / (1.25 - 0) = -32 feet/second
Step-by-step explanation:
Given
If x = 4 Then
3x + 1 = 3 * 4 +1 = 12 + 1 = 13
The answer should be 13.
-4x + 8y = 2
-4x + 4x + 8y = 2 + 4x
8y = 4x + 2
8 8
y = ¹/₂x + ¹/₄
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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I think it is y= 9/5 + 4x/5
