Answer:
.
Step-by-step explanation:
Answer:
It is B, 7 points.
Step-by-step explanation:
You can start from the point (8,2) and then you go up 7 points to reach the point (8,9) which is 7 points.
You can sort them on the shapes that are the same or different
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:
Step-by-step explanation:
π/6 is its own reference angle; it's positive and between 0 and π/2.
