78
(6x)(5y)
i know that a feasible region, is the region which satisfies all the constraints but i don't know exactly why is the unshaded region regarded as a feasible region instead of the shaded region.
It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.
Given definitions, or descriptions at least, of "point D" and "the feasible region",I might have had a shot at answering this one.
Unfortunately, limitations of the browser used by Answers.com means that we cannot see most symbols. It is therefore impossible to give a proper answer to your question. Please resubmit your question spelling out the symbols as "plus", "minus", "equals", "squared", "cubed" etc.Furthermore, there is no information on what the feasible region is so, even if the browser worked perfectly, it would have been impossible to answer the question.Unfortunately, limitations of the browser used by Answers.com means that we cannot see most symbols. It is therefore impossible to give a proper answer to your question. Please resubmit your question spelling out the symbols as "plus", "minus", "equals", "squared", "cubed" etc.Furthermore, there is no information on what the feasible region is so, even if the browser worked perfectly, it would have been impossible to answer the question.Unfortunately, limitations of the browser used by Answers.com means that we cannot see most symbols. It is therefore impossible to give a proper answer to your question. Please resubmit your question spelling out the symbols as "plus", "minus", "equals", "squared", "cubed" etc.Furthermore, there is no information on what the feasible region is so, even if the browser worked perfectly, it would have been impossible to answer the question.Unfortunately, limitations of the browser used by Answers.com means that we cannot see most symbols. It is therefore impossible to give a proper answer to your question. Please resubmit your question spelling out the symbols as "plus", "minus", "equals", "squared", "cubed" etc.Furthermore, there is no information on what the feasible region is so, even if the browser worked perfectly, it would have been impossible to answer the question.
It would depend on the feasible region.
26
maximum value of 6y+10y
42
14
(6x)(5y)
The answer obviously depends on what the boundaries of the feasibility region are.
To find the maximum value of 2x + 5y within the feasible region, you would need to evaluate the objective function at each corner point of the feasible region. The corner points are the vertices of the feasible region where the constraints intersect. Calculate the value of 2x + 5y at each corner point and identify the point where it is maximized. This point will give you the maximum value of 2x + 5y within the feasible region.
Oh, dude, the maximum value of 3x + 4y in the feasible region is like finding the peak of a mountain in a math problem. You gotta plug in the coordinates of the vertices of the feasible region and see which one gives you the biggest number. It's kinda like finding the best topping for your pizza slice in a land of math equations.
definition feasible region definition feasible region
To find the maximum value of 3x + 3y in the feasible region, you will need to determine the constraints on the variables x and y and then use those constraints to define the feasible region. You can then use linear programming techniques to find the maximum value of 3x + 3y within that feasible region. One common way to solve this problem is to use the simplex algorithm, which involves constructing a tableau and iteratively improving a feasible solution until an optimal solution is found. Alternatively, you can use graphical methods to find the maximum value of 3x + 3y by graphing the feasible region and the objective function 3x + 3y and finding the point where the objective function is maximized. It is also possible to use other optimization techniques, such as the gradient descent algorithm, to find the maximum value of 3x + 3y within the feasible region. Without more information about the constraints on x and y and the specific optimization technique you wish to use, it is not possible to provide a more specific solution to this problem.
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