Hi,
Let the
Youngest brothers age = x+2
Middle brothers age = x+4
Oldest brothers age = x+6
So,
Youngest brothers age = x+2 = 28+2 = 30 years of age
Middle brothers age = x+4 = 28+4 = 32 years of age
Oldest brothers age = x+6 = 28+6 = 34 years of age
Have a great day!
Since y=3x+15
sub 3x+15 for y in other equation
2x+3(3x+15)=1
2x+9x+45=1
minus 45
11x=-44
divide 11
x=-4
sub
y=3x+15
y=3(-4)+15
y=-12+15
y=3
(-4,3)
Answer: i dont know the answer
36= 12.5% because 25%+62.5%= 87.5%
So 100%-87.5%=12.5%
87.5%-12.5%= 75%
36+36=72=25%
72+72+72+72= 288 = 100%
So the total number of pages in the magazine is 288
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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