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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Answer:
5
Step-by-step explanation:
3-(-2)=5
Now to solve this problem, all we have to remember is the
formula for calculating the linear speed given the radial speed, that is:
v = r w
where,
v = is the linear velocity or linear speed
r = is the radius of the circular disk = (1 / 2) diameter
= (1/ 2) (2.5 inches) = 1.25 inches
w = is the radial velocity (must be in rad per time) =
7200 rev per minute
Calculating for v:
v = 1.25 inches (7200 rev per minute) (2 π rad / 1 rev)
v = 56,548.67 inches / minute
Converting to miles per hour:
v = 56,548.67 inches / minute (1 mile / 63360 inches) (60
min / hour)
<span>v = 53.55 mile / hour</span>
The numbers 7, 11, 12, 13, 14, 18, 21, 23, 27, and 29 are written on separate cards, and the cards are placed on a table with th
Sophie [7]
there are a total of 10 cards
there are 3 cards with even numbers
so you have a 3/10 probability of picking an even n umber
