<span>5.497211734394065. radius confirmed!</span>
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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900% of 450=4050
To get the solution, we are looking for, we need to point out what we know.
1. We assume, that the number 450 is 100% - because it's the output value of the task.
2. We assume, that x is the value we are looking for.
3. If 450 is 100%, so we can write it down as 450=100%.
4. We know, that x is 900% of the output value, so we can write it down as x=900%.
5. Now we have two simple equations:
1) 450=100%
2) x=900%
where left sides of both of them have the same units, and both right sides have the same units, so we can do something like that:
450/x=100%/900%
6. Now we just have to solve the simple equation, and we will get the solution we are looking for.
7. Solution for what is 900% of 450
450/x=100/900
(450/x)*x=(100/900)*x - we multiply both sides of the equation by x
450=0.111111111111*x - we divide both sides of the equation by (0.111111111111) to get x
450/0.111111111111=x
4050=x
now
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