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
brainly.com/question/16382372
#SPJ4
Answer:
The correct option is 1.148 < σ < 6.015
Explanation:
The 99% confidence interval for the standard deviation is given below:

Where:


Therefore, the 99% confidence interval is:


Therefore, the option 1.148 < σ < 6.015 is correct
After running for 18 minutes, Julissa completes 2 kilometers. If she is running a 10-kilometer race at a constant pace, then she comletes 1 kilometer after running 9 minutes. Now you can find the distance she run after 1 minute. This is
km.
Let t be the time in minutes. Then k, the number of kilometers, can be found from proportion:
- 1 minute
k km - t minutes.
Thus,
Answer:
Answer:

Step-by-step explanation:
Do FOIL, first outside inside last
3x × 3x = 9x^2
3x × -7 = -21x
7 × 3x = 21x
7 × -7 = -49

So it would just be 9x^2 - 49
Answer:
(A-c)/8 = b
Step-by-step explanation:
A = 8b +c
A - c = b
(A-c)/8 = b
Hope this helps you!