Answer:
-0.05
Step-by-step explanation:
Given the expression :
0.1×( – 0.9+ – 0.2÷ – 0.5)
Evaluating the bracket :
0.1 * (-0.9 - 0.2 ÷ - 0.5)
Solving the division first
-0.2 ÷ - 0.5 = 0.4
Now we have ;
0.1 * (-0.9 + 0.4)
0.1 * - 0.5
= - 0.05
Answer: The answer is B.
Step-by-step explanation: The scale on the y-axis could be changed to 100–120.
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:
The answer is A. 6/17
Step-by-step explanation:
the top number is how many wrenches and the bottom number is all of the tools you could possibly get.
