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.
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Answer:
z = 18
x = 26 and y = 16
Step-by-step explanation:
Given:


Rewrite
to make
the subject:

Substitute this into
:

Cross multiply:




Substitute found value of x into
and solve for y:


Substitute found value of y into
and solve for z:

Answer:
141.6
Step-by-step explanation:
Answer:
E. None of these answers is reasonable.
Step-by-step explanation:
Customary units are inches, feet, yards, and miles.
Since,
1 foot = 0.3048 meters,
⇒ 1 meter =
feet,
17.42 meters =
feet



Now, 1 feet = 12 inches,
⇒
feet =
inches
= 
Hence,
17.42 meters = 
= 57' 232/127''
That is, None of these answers is reasonable.