Answer:
b+3a
Step-by-step explanation:
MN=AN+AM
AN=0.5*AB
AB=OA+OB=4a+2b
AN=2a+b
AM=OA-OM=4a-3a=a
MN=2a+b+a=3a+b
Answer:
1418.03
Step-by-step explanation:
86.50 is 6.1% of what amount?
Multiply 86.50 x 100 = 8650
Divide by 6.1 = 1418.03
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:
it was doing that to me too, you just have to keep answering and eventually it will come i was waiting a week on virtuoso with all the points and brainliest i needed
Step-by-step explanation:
