What do u need help with ????
195xy is ur lowest common denominator
This will be false a box plot is also known as quantile plots
Number of cone left is 75 cone
<u>Given that;</u>
Cost of each cone = $3.75 each
Starting number of cone = 300
Total amount earn = $843.75
<u>Find:</u>
Number of cone left
<u>Computation:</u>
Number of cone sale = Total amount earn / Cost of each cone
Number of cone sale = 843.75 / 3.75
Number of cone sale = 225 cone
Number of cone left = Starting number of cone - Number of cone sale
Number of cone left = 300 - 225
Number of cone left = 75 cone
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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.
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