Answer:
(100×m) + d
where m is the month number of your birthday
and d is the day number of your birthday
Step-by-step explanation:
[(((m × 4) + 14) × 25) - 230] + d - 120
》[((4m + 14) × 25) - 230] + d - 120
》100m + 350 - 230 -120 + d
》100m + d
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:
x = 1/9
Step-by-step explanation:
8x + 2 = 3 - x
Add x to both sides.
9x + 2 = 3
Subtract 2 from both sides.
9x = 1
Divide both sides by 9.
x = 1/9
Answer:
A, B, D, G, and F
Step-by-step explanation:
