Answer:
y ≥ −4x − 2
Step-by-step explanation:
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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Answer:
C
Step-by-step explanation:
Multiply ben (y) by 4 since dave has 4x more and add % since jeff has 5 more than Daves total
Hope this helps ^_^
Answer:
a)1.5
b)36
c)34
d)5.33333...
e)0
Step-by-step explanation:
