Answer:
x=90 y=58 z=32
Step-by-step explanation:
I don't know how to explain it but yea
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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Step-by-step explanation:
it will be 14 times 2 which will be 28 length and 74 plus 74 which will be 148 for the both side wide.
Answer:
-y = 25
y = -25
When the signs are different you add, the value for x is the same so you don't need to do anythung else but to add
Answer:
10.392305a21b93
=10.392305a21b93
Step-by-step explanation:
