Answer:
4.8278117e+22
Step-by-step explanation:
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:
Mass = 1.3 Kilograms.
Step-by-step explanation:
Given the following data;
Force = 55.1 N
Acceleration = 41.6 m/s²
To find the mass of the stone in kg;
Newton's Second Law of Motion states that the acceleration of a physical object is directly proportional to the net force acting on the physical object and inversely proportional to its mass.
Mathematically, it is given by the formula;
Force = mass * acceleration
Substituting into the formula, we have;
55.1 = mass * 41.6
Mass = 1.3 Kilograms
Answer:
he is currently 34
Step-by-step explanation:
x + 6 = 5(x-26)
x + 6 = 5x - 130
+130 +130
x + 136 = 5x
-x -x
136 = 4x
/4 /4
x = 34
You can check your work as well:
34 + 6 = 40
34 - 26 = 8
8 x 5 = 40
That would be f^1/4.
1/4 being the rational exponent
