Answer:
their are different sizes of soup bowls
Answer:
I think it is C but can't be sure because B isn't clear.
Step-by-step explanation:
Answer:
a) We can just add Distance from Tulsa to Dallas and distance from Dallas to Houston,
258 + 239 = 497
b) It's the same distance = 497
c) We can tell its the same distance, because we are travelling in the same road, which would mean it would be no different when going to or coming back from Houston.
Answer:
4
Step-by-step explanation:
the side Q-R corresponds to K-L
just as O-P corresponds to K-J
K-J= 36
O-P=6
36/6=6
so 24/6=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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