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:
z=44
Step-by-step explanation:
One way to go about this with the given angles is to use the alternate interior angles theorem to find the full measure of ∠C and ∠A. Then, use the same theorem to set ∠BDA as 52°. Subtract all the angle measures from 360 (because the sum of the interior angles of a quadrilateral is = 360 degrees). Then divide the result by 2.
360-84-84-52-52=88
=88/2
=44
So z=44°
The first given equation is:
4x + 3y = 6
which can be rewritten as:
2(2x) + 3y = 6 .............> equation I
The second given equation is:
2x + 2y = 5
which can be rewritten as:
2x = 5 - 2y ........> equation II
Substitute with equation II in equation I to get the value of y as follows:
2(5-2y) + 3y = 6
10 - 4y + 3y = 6
-y = 6-10 = -4
y = 4
Substitute with the y in equation II to get x as follows:
2x = 5 - 2y
2x = 5 - 2(4)
2x = 5 - 8 = -3
x = -3/2
From the above calculations:
x = -3/2
y = 4
Answer: $30.60
Step-by-step explanation:
multiply 3.4% or 0.034 in decimal form, by 900, and that’s your interest
Answer:
13=u
Step-by-step explanation:
54/4 = 13
