Given:
The graph of a downward parabola.
To find:
The domain and range of the graph.
Solution:
Domain is the set of x-values or input values and range is the set of y-values or output values.
The graph represents a downward parabola and domain of a downward parabola is always the set of real numbers because they are defined for all real values of x.
Domain = R
Domain = (-∞,∞)
The maximum point of a downward parabola is the vertex. The range of the downward parabola is always the set of all real number which are less than or equal to the y-coordinate of the vertex.
From the graph it is clear that the vertex of the parabola is at point (5,-4). So, value of function cannot be greater than -4.
Range = All real numbers less than or equal to -4.
Range = (-∞,-4]
Therefore, the domain of the graph is (-∞,∞) and the range of the graph is (-∞,-4].
Explanation:
The formula isnt correctly written, it should state:
You have to start from 
and end in a³+b³. On your first step, you need to use the distributive property.
This is equal to
Note that the second term, -a²b, is cancelled by the fourth term, ba², and the third term, ab², is cancelled by the fifht term, -b²a. Therefore, the final result is a³+b³, as we wanted to.
Answer:
Numerator , 0
Anything divided by 0 is always 0
Brainliest Pls
Answer:
∠A = 30°
∠B = 60°
∠C= 90°
Step-by-step explanation:
This is a right triangle, you can see it mainly by the red square in C, and it is always used to mark 90 degrees.
Knowing that, you now know <em>∠C is 90°</em>
Now, to find ∠B, you should use the following equation:
This means that the sum of the three angles of a triangle gives 180. ALWAYS. So to find the missing angle, ∠B, do the following:
Fill the values of the equation with the angles you now know:
Solve the equation, passing the 30° and 90° to the other side of the equal sing with Inverse Operation:
<em>B = 60</em>
<em>Hope it helps!!</em>
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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