Answer:
C -1
Step-by-step explanation:
Do you mean the probability of the die landing on a specific number? If so it would be 16.6 with 6 continuing on forever.
Answer:
1. distance = sqrt( (7-7)^2+(2- -8)^2) = 10
2. check out desk (0,0 ) => distance = sqrt( (0- -9)^2+(0-0)^2) = 9
3. last corner ( -3, 4)
4. area = sqrt( (-10- -10)^2+(10-4)^2) x sqrt( (-3- -10)^2+(10-10)^2) = 6x7 =42
5. check desk (0,0), south direction = negative y axis => P_beginning (0,-20), P_end (0,-(20+25)) = (0,-45)
6. A(-2,-1) and B(4,-1) lie in y =-1. AB = sqrt( (-2- 4)^2+(-1- -1)^2) =6
=> area = 3.6x6 =21.6
=> peri = 2x(3.6+6) = 19.2
7. A(-5,4) and B(2,4), AB = sqrt( (-5- 2)^2+(4- -4)^2) = 7 => AB is base
=> p = peri = 7+ 8.3x2 = 23.6
=> area = sqrt[px(p-7)x(p-8.3)x(p-8.3)]
=sqrt[23.6x(23.6-7)x(23.6-8.3)x(23.6-8.3)] = 302.8
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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