Answer:
we have
Complete the square. Remember to balance the equation by adding the same constants to each side
Rewrite as perfect squares
Square Root both sides
substitute
therefore
the answer is
the solutions are
or
or
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RStep-by-step explanation:
Answer:
. It’s because a prime factor of all even numbers is 2. Therefore the prime factors of all even squares includes two, therefore, the square must be even.
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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Yes because the perimeter of the bulletin is 23 feet. this is because to find perimeter you use 2(L)+2(W).
