Answer:
Distance: 8.5440037453175
Step-by-step explanation:
Distance: 8.5440037453175
Steps:
Distance (d) = √(-7 - -10)2 + (-1 - -9)2
= √(3)2 + (8)2
= √73
= 8.5440037453175
Slope and Angle:
ΔX = -7 – -10 = 3
ΔY = -1 – -9 = 8
Slope (m) =
ΔY
ΔX
=
8
3
= 2.6666666666667
θ =
arctan( ΔY )
ΔX
= 69.443954780417°
Equation of the line:
y = 2.6666666666667x + 17.666666666667
or
y =
8 x
3
+ 53
3
When x=0, y = 17.666666666667
When y=0, x = -6.625
300 ml ... 200 ml
75 ml ... x = ?
If you would like to know what is the final volume of a gas, you can calculate this using the following steps:
300 * x = 200 * 75 /300
x = 200 * 75 / 300
x = 50.0 ml
The correct result would be A. 50.0 ml.
98cm^2 - bigger triangles are 28 each, bottom one is 14. (28 x 3) + 14=98
Answer:
idk you tell me
Step-by-step explanation:
1. you should tell me
2. i write the answer when you tell me
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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