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.
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Answer:
James needs to earn 78 more dollars.
Step-by-step explanation:
If you add up all of the money James has earned, (1 + 7 + 14), it equals 22. If you subtract 22 from 100, it equals 78. Therefore James needs to earn 78 more dollars.
That’s confusing do you know the formula your supposed to use?
5x + x+ 54 + 90= 180
6x+ 54+ 90= 180
6x+ 144 = 180
minus 144 on both sides
6x= 36
divide by 6 on both sides
x= 6
