Answer: 5.5
Step-by-step explanation:
Plugging it into the formula, we get
2x-3 = 8
Add 3 to both sides:
2x = 11
Divide by 2 on both sides:
x = 5.5
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:


Step-by-step explanation:
Given



Required
Find P(A) and P(B)
We have that:
--- (1)
and
--- (2)
The equations become:
--- (1)

Collect like terms


Make P(A) the subject

--- (2)


Substitute: 
![[0.770 - P(B)] * P(B) = 0.144](https://tex.z-dn.net/?f=%5B0.770%20-%20P%28B%29%5D%20%2A%20P%28B%29%20%3D%200.144)
Open bracket

Represent P(B) with x

Rewrite as:

Expand

Factorize:
![x[x - 0.45] - 0.32[x - 0.45]= 0](https://tex.z-dn.net/?f=x%5Bx%20-%200.45%5D%20-%200.32%5Bx%20-%200.45%5D%3D%200)
Factor out x - 0.45
![[x - 0.32][x - 0.45]= 0](https://tex.z-dn.net/?f=%5Bx%20-%200.32%5D%5Bx%20-%200.45%5D%3D%200)
Split

Solve for x

Recall that:

So, we have:

Recall that:

So, we have:


Since:

Then:


Answer: AB ≅ DE
∠BAC≅∠DEF
Step-by-step explanation:
as AAS is a criterion where two angles and a side not included between the angles is congruent with the other triangle's two angles and the side not included between the angles.
Answer:
1.88 is a irrational number