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Fofino [41]
3 years ago
12

Avery wants to put a percentage of muffins in a display case. There are 60 muffins total. 40% are blueberry, 20% are pumpkin, an

d 30% are cranberry. how many blueberry muffin can she put in the display
Mathematics
1 answer:
padilas [110]3 years ago
5 0

i cant do this either

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Which of the following points is not coplanar with polnis C, D and E?
Leokris [45]

Answer: Point R

===================================================

Explanation:

The term "coplanar" means "points that are in the same plane".

Points A, S and U are all in the base plane. Think of this as the floor of the pyramid, so to speak. These three points are with C, D, and E, which are also on the base plane or floor plane. So we can say that all of these points are coplanar.

In contrast, point R is not on the floor plane. It's higher up. So that's why R is not coplanar with points C,D,E.

7 0
3 years ago
Lola rolls two fair number cubes. What is the probability that the two numbers Lola rolls include at least one 4 and have a prod
Oxana [17]
Lola rolls two fair number cubes what is the probability that the two numbers Lola rolls include at least one 4 and have product of at least 16? Probably half of a chance
3 0
3 years ago
Given the following image, find the angle of rotation and the number of times each figure can rotate.​
Dmitriy789 [7]

Rotated: The figure can be rotated  2  times.  Angle of Rotation:  180 ∘

7 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 is a segment bisector
ruslelena [56]

A segment bisector is a segment, ray, line, or plane that intersects a given segment at its midpoint.

For example, in the diagram shown, line SQ bisects segment PR because line SQ intersects segment PR at its midpoint which is Q.

7 0
3 years ago
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