Answer:
-14
Step-by-step explanation:
Multiply the is together. Multiply the j's together. Add the answer. The answer is a scalar.
(7 × -2) + (5 × 0)
= -14
$0.42 that is what your answer would be
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
brainly.com/question/16382372
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Answer:
subtract 2.4 from both sides, simplify to -3x=16.8, divide by -3, simplify to x=-5.6.
Step-by-step explanation:
-3x + 2.4 = 19.2
v <u>-2.4</u> <u>-2.4</u>
v 0 16.8
<u>-3x</u>=<u>16.8</u>
-3 -3
x=-5.6
hope this helps,
<h3>Noelle</h3>
