Answer:
9.6 ounces of butter, 8 ounces of sugar, 12 ounces of flour, 4 eggs
Explanation: 20 / 16 = 4/5. 16 is four-fifths of 20. So multiply 4/5 with everything.
12 * 4/5 = 9.6 ounces
10 * 4/5 = 8 ounces
15 * 4/5 = 12 ounces
5 * 4/5 = 4 eggs
It is often more convenient to evaluate a polynomial when it is written is "Horner form."
... f(x) = (((10x -4)x -8)x +3)x -6
The graphs offered can be distinguished by their values of f(1) and f(2), so our table can be a short one.
... f(1) = (((10·1 -4)1 -8)1 +3)1 -6 = -5 . . . . . . . eliminates graph d
... f(2) = (((10·2 -4)2 -8)2 +3)2 -6 = 96 . . . . eliminates graphs a and c
The appropriate choice is b.
I think its runner B because its lower down then runner A 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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