Answer:
75%
Step-by-step explanation:
900 /1200= .75=75%
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:
x = 2 /3
Step-by-step explanation:
x^3 = 8/27
Take the cube root of each side
(x^3)^1/3 = (8/27)^1/3
We know that (a/b) ^c = a^c / b^c
x = (8 ) ^1/3 / (27)^1/3
x = 2 /3
