Answer:
25%- 1875 Votes
<em>7500 x 0.55 = 4125 (</em><em>first candite valid votes</em><em>)</em>
<em>7500 x 0.20 = 1500 (</em><em>invalid</em><em>)</em>
<em>first candite valid votes</em><em> </em><em>(</em><em>55</em><em>) + invalid votes (</em><em>20</em><em>) = 75% of total votes </em>
<em>first candite valid votes</em><em> </em><em>(</em><em>4125</em><em>) + invalid votes (</em><em>1500 </em><em>) = 5625 of total votes </em>
<em />
<em>100 (</em><em>total</em><em> </em><em>percent of votes</em><em>) - 75 (</em><em>total percent of votes</em><em>) = 25% Votes Left</em>
<em>7500 (</em><em>total</em><em> </em><em>number of votes</em><em>) - 5625 (</em><em>total number of votes</em><em>) = 1875 Votes Left</em>
<em>7500 x 0.25 = 1875 (</em><em>valid votes for the other candite</em><em>)</em>
<em> </em>
Answer:girl use google
Step-by-step explanation:what
First, disregard the sign for absolute value and solve for x.
2x - 1 = 3x + 5
2x - 3x = 5 + 1
-x = 6
x = -6
Now, you have to interpret the absolute value. The absolute value of x is always the positive version of whatever the number is. Since x = -6, its absolute value is 6. Therefore, the possible value for x is only one, which is 6.
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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