Answer:
the angle of elevation is 12.56°
Step-by-step explanation:
the height of the ramp represents the opposite side and the length of the ramp the hypotenuse
we see that it has (angle, hypotenuse, opposite)
well to start we have to know the relationship between angles, legs and the hypotenuse
a: adjacent
o: opposite
h: hypotenuse
sin α = o/h
cos α= a/h
tan α = o/a
we choose the one with opposite and hypotenuse
sin α = o/h
sin α = 5ft / 23ft
sin α = 5/23
α = sin^-1 ( 5/23)
α = 12.56°
the angle of elevation is 12.56°
Answer:
18
Step-by-step explanation:
4 divided by 24 is 6
6x3=18
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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By comparing the perimeters, we can deduce the scaling factor:
The areas scale with the square of the scaling factor, so the new area is
Answer:
just calculate
Step-by-step explanation:
