Answer:
<u>Answer</u><u>:</u><u> </u><u>B</u>
For the first problem the answer is -5/2
2x−7+10=−2
Step 1: Simplify both sides of the equation.
2x−7+10=−2
2x+−7+10=−2
(2x)+(−7+10)=−2
(Combine Like Terms)
2x+3=−2
Step 2: Subtract 3 from both sides.
2x+3−3=−2−3
2x=−5
Step 3: Divide both sides by 2.
2x/2=−5/2
x=−5/2
Answer:
x= -5/2
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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From the information we have, we know that each book costs 1.20 dollars. So we shall say this price is at 100% .
Now we need to form an equation to get the percentage when the books are sold at 0.80 dollars.
100% = 1.20
x = 0. 80
Where x is the new percentage when the sale is at 0.80 dollars.
We cross multiply the equation:
1.20 *x = 0.80 * 100
1.2x = 80
x = 80/1.2
x = 66.7%
Round off 66.7 to the nearest tenth we get 67%
The notebooks are sold at 67% (of the original cost).
