1.) 3p
2. -b+3
3. 6x
4. -3p
5. -4v
6. r
7. 9-4r
8. 4b+2
9. 14n
10. 4b+2
11. 35n+45
12. -82v
13. 38n
14. 38x
Answer:
The answer is 94.2096774194
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:
512
Step-by-step explanation:
Answer:
The side lengths are not congruent. Evidence: Becuase the line shows that one is higher. None of the sides are parallel. Evidence: none of the lines of the shape are on the exact same line. No the adjacent sides are not perpendicular. Evidence: They are crooked and you can tell becuase of how the shape is on the line. The most specific shape is a rhombus. A rhombus is a crooked square. This isn't a perfect rhombus.
Step-by-step explanation:
