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maria [59]
2 years ago
9

Pls help I will give brainleist (2)

Mathematics
2 answers:
mihalych1998 [28]2 years ago
5 0

Answer:

D. 0.19

Step-by-step explanation:

Have a great day

Georgia [21]2 years ago
3 0

Answer:

  • D. 0.19

Step-by-step explanation:

Look at the intersection of the row "Tenth grade" and the column "Internet"

The number in the relevant cell is 34.

<u>Divide 34 by the total number which is 175:</u>

  • 34/175 = 0.19 (rounded)

Correct choice is D

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Water freezes at 0 degrees Celsius. A student discovered that adding half a cup of salt to a gallon of water lowers its freezing
Artemon [7]

Answer:

-7°C

Step-by-step explanation:

Freezing point of water = 0°C

By adding salt, freezing point lowers by 7°C ;

The freezing point of salt water can be expressed as ;

Since temperature lowers :

Freezing point of salt water :

Freezing point of water - 7°C

0°C - 7°C

= - 7°C

4 0
3 years ago
The scale on a map is 3 inches for every 6 1/4 miles. If the distance on the map is 5 1/2 inches, how far apart are the cities i
vovikov84 [41]
I think it is 34 3/8
4 0
3 years ago
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
What type of function is f(x)=2(1/7)^x?
Archy [21]

Answer:

B) Exp. Decay

Step-by-step explanation:

Because the term getting raised to a power is under 1, the function immediately starts going down and approaches negative infinity as x approaches infinity.

5 0
3 years ago
A 3 cm x 3 cm rectangle sits inside a circle with radius of 4 cm.
Ivanshal [37]

Step-by-step explanation:

you did not show us the picture or described what the shaded area is.

I assume it is the area of the circle without the inner square (as the "rectangke" is actually a square, because its sides are equally long).

so, we need to calculate the area of the circle and subtract the area of the square.

the area of the square is 3×3 = 9 cm².

the area of the circle is

pi×r²

with r being the radius.

so,

pi×4² = pi×16 = 16pi

the shaded area is then

16pi - 9 = 41.26548246... cm² ≈ 41.27 cm²

nothing of the answer options seems to be in that area, so maybe my assumption about the shaded area is wrong. or the specified answer options are mistyped. or both.

anyway, without additional information that is the best I can do.

8 0
1 year ago
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