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Marysya12 [62]
3 years ago
13

You make $38,000 per year. You have been offered a promotion and a $5,000 raise per year. Your average federal income tax rate w

ill go up from 12% to 16% per year. Should you take the new job and the raise? how many weeks are in a year
Mathematics
1 answer:
Tom [10]3 years ago
3 0

Answer: yes

Step-by-step explanation:

You might be interested in
What is the following product? square root 30 times square root 10
egoroff_w [7]

Answer:

D. 10 square root 3 is your answer

Step-by-step explanation:

Here is what you do.

<em>square root 30 = 3 x 2 x 5 </em>

<em>square root 10 = 2 x 5</em>

Add them together.

<em>3 x 2 x 2 x 5 x 5 </em>

<em>3 x 4 x 25.</em>

4 and 25 can come out of the square root.

<em>2 x 5 = 10</em>

10 square root 3 is your answer.

4 0
2 years ago
Which equation is true for the value a = 7?
professor190 [17]
THE first option is correct
7 0
3 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
triangle MNO is an equilateral triangle with sides measuring 16 units What is the height of the triangle?
fiasKO [112]

see the attached figure to better understand the problem

we know that

The equilateral triangle has three equal sides

so

in the equilateral triangle ABC

AB=BC=AC=16 units

the height of the triangle is the segment BD

in the right triangle BCD

Applying the Pythagorean Theorem

BC^{2} =BD^{2}+DC^{2}

solve for BD

BD^{2}=BC^{2}-DC^{2}

substitute the values

BD^{2}=16^{2}-8^{2}

BD^{2}=192

BD=\sqrt{192}\ units

therefore

<u>the answer is</u>

the height of the triangle is \sqrt{192}\ units


7 0
3 years ago
Read 2 more answers
6 m
dimulka [17.4K]

Answer:

51.63m^3

Step-by-step explanation:

The figure is made up of a rectangle, triangle and a semi circle

Area of the figure = Area of triangle + rectangle + semicircle

Area of the triangle = 1/2 * base * height

Area of the triangle = 1/2 * 3 * 5

Area of the triangle = 15/2

Area of the triangle = 7.5m^3

Area of the rectangle = Length * Width

Area of the rectangle = 6 * 5

Area of the rectangle = 30m^2

Area of the semicircle = πr²/2

Area of the semicircle = π(3)²/2

Area of the semicircle = 3.14(9)/2

Area of the semicircle = 3.14 * 4.5

Area of the semicircle = 14.13m^2

The area = 30 + 14.13+7.5

Area of the figure = 51.63m^3

7 0
3 years ago
Read 2 more answers
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