We can use the difference of two squares, which is:
x^2 + y^2 = (x-y)(x+y)
So,
(9m-7)(9m+7)
Hope this helps!
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:
not a function.
Step-by-step explanation:
the x value/set a has to have 1 line from each but the -3 has 2 lines, therefore not a function
Answer:
1-x
Step-by-step explanation:
1/50= 0.02
10/50= 0.2
10-50= -40
1-50= -49
-49 has the lowest value of all of these equations.