Answer:
78.5÷3.14=25
56.272×41.2=2318.4064
429×338×712=103241424
447÷513=0.8713450292
Step-by-step explanation:
hope it will help!
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:
5. -2 3/4, -2.2, 2.8, 3 1/8
6. -0.6 , 0.65 , 2/3 , 4/5
Step-by-step explanation:
Answer:01 50 Hrs
Step-by-step explanation:
If the soldier started patrol at 2230 hrs and his petrol lasted for 3 hours and 20 minutes, that means he was on patrol for that time.
The time he finished was therefore:
= 2230 + 320
= 2550
At 24 you begin at 0:00am so:
= 2550 - 2400
= 150
Patrol ended at 0150 hrs
