Answer:
the 3rd one from top
Step-by-step explanation:
because the coefficient is largest
Answer:
42°
Step-by-step explanation:
The interior angles of a triangle always add to 180 degrees.
90+48+x=180
x=180-90-48
=42
x is 42°
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:
42/50, 21/25
Step-by-step explanation:
84/100=42/50=21/25
