Answer:
There's no picture for us to refer to. I need a picture to answer the question.
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:
636w
Step-by-step explanation:
producto = ×
Here, we have to examine the equation of the straight line which is denoted by: y = m x +c where "m" is the slope which represents the steepness and c is the y-intercept
Here, the two linear functions have the same slope "m" and the same y-intercept "c". When both these are the same, the two linear functions are representing the same straight line.
Therefore, Jeremy is correct in his argument.
Please insert a image but i think the answer would be C or B
