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Vesnalui [34]
2 years ago
13

I need help ASAP Find y.

Mathematics
2 answers:
Sladkaya [172]2 years ago
5 0

Answer:

7√2

Step-by-step explanation:

Blizzard [7]2 years ago
4 0

Answer:

<h2>315 i Calaulated it  </h2>

Step-by-step explanation:

I know it is right trust me and if it is wrong srry but you will get it right

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4x+11y=7<br><br>4x+3y=-1<br>what are the steps
andreev551 [17]

Answer:

x = -1, y = 1

Step-by-step explanation:

Given that:

\begin{bmatrix}4x+11y=7\\ 4x+3y=-1\end{bmatrix}

Isolate x for 4x + 11y = 7 which ⇒ x=\frac{7-11y}{4}

Substitute x=\frac{7-11y}{4} into the equation: which is:

\begin{bmatrix}4\cdot \frac{7-11y}{4}+3y=-1\end{bmatrix}

Drop Down to:

\begin{bmatrix}7-8y=-1\end{bmatrix}

Isolate y for 7-8y = -1: y = 1

Thus, x=\frac{7-11y}{4}

Substitute y = 1

Hence,

x=\frac{7-11\cdot \:1}{4}=-1

Solution ⇒ x = -1 and y = 1

<u><em>~learn with lenvy~</em></u>

6 0
2 years ago
Read 2 more answers
Provide an example of optimization problem
Mashutka [201]

Answer:

a. Convex solutions ,GO Methods

b. market efficiency

Explanation :

Step-by-step explanation:

A globally optimal solution is one where there are no other feasible solutions with better objective function values. A locally optimal solution is one where there are no other feasible solutions "in the vicinity" with better objective function values. You can picture this as a point at the top of a "peak" or at the bottom of a "valley" which may be formed by the objective function and/or the constraints -- but there may be a higher peak or a deeper valley far away from the current point.

In convex optimization problems, a locally optimal solution is also globally optimal. These include LP problems; QP problems where the objective is positive definite (if minimizing; negative definite if maximizing); and NLP problems where the objective is a convex function (if minimizing; concave if maximizing) and the constraints form a convex set. But many nonlinear problems are non-convex and are likely to have multiple locally optimal solutions, as in the chart below. (Click the chart to see a full-size image.) These problems are intrinsically very difficult to solve; and the time required to solve these problems to increases rapidly with the number of variables and constraints.

GO Methods

Multistart methods are a popular way to seek globally optimal solutions with the aid of a "classical" smooth nonlinear solver (that by itself finds only locally optimal solutions). The basic idea here is to automatically start the nonlinear Solver from randomly selected starting points, reaching different locally optimal solutions, then select the best of these as the proposed globally optimal solution. Multistart methods have a limited guarantee that (given certain assumptions about the problem) they will "converge in probability" to a globally optimal solution. This means that as the number of runs of the nonlinear Solver increases, the probability that the globally optimal solution has been found also increases towards 100%.

Where Multistart methods rely on random sampling of starting points, Continuous Branch and Bound methods are designed to systematically subdivide the feasible region into successively smaller subregions, and find locally optimal solutions in each subregion. The best of the locally optimally solutions is proposed as the globally optimal solution. Continuous Branch and Bound methods have a theoretical guarantee of convergence to the globally optimal solution, but this guarantee usually cannot be realized in a reasonable amount of computing time, for problems of more than a small number of variables. Hence many Continuous Branch and Bound methods also use some kind of random or statistical sampling to improve performance.

Genetic Algorithms, Tabu Search and Scatter Search are designed to find "good" solutions to nonsmooth optimization problems, but they can also be applied to smooth nonlinear problems to seek a globally optimal solution. They are often effective at finding better solutions than a "classic" smooth nonlinear solver alone, but they usually take much more computing time, and they offer no guarantees of convergence, or tests for having reached the globally optimal solution.

5 0
3 years ago
Gavin drank 4 5 liter of water Monday before going jogging. He drank 2 3 liter of water after his jog. How much water did Gavin
Hoochie [10]

Answer:

<h2>8.0 liters</h2>

Step-by-step explanation:

The question is not well-formatted, this is supposed to be the correct format

<em>Gavin drank 4.5 liter of water Monday before going jogging. He drank 2.3 liter of water after his jog. How much water did Gavin drink altogether? Write your answer as a mixed number.</em>

Given

we are told that he first drank 4.5 liters of water before jogging

after jogging he drank 2.3 liters

Required

Total amount of water drank

=4.5+3.5

=8.0liters

5 0
2 years ago
Find the volume of the composite solid. Round your answer to the nearest hundredth.
Vera_Pavlovna [14]

Answer: see my work

Step-by-step explanation:

volume is lwh

volume is 3*3*10

volume is 9*10

volume is cubic cm

volume is 90

volume is 90 cubic cm

8 0
2 years ago
Read 2 more answers
Ms. Wall will roll a single number cubes. What is the probability that she will roll an even number?
Arturiano [62]
Well single number, meaning from 1 - 9. She has a higher chance of getting an odd number for sure, as there are more odd numbers on the dice :) So, the ratio is 4:5 and fraction: 4/5. %<span>44.444 is your answer :) 

Thank you,
Darian D.</span>
4 0
3 years ago
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