
Solve the following exercise using jupyter notebook for Python, to find the objective function, variables, constraint matrix and print the graph with the optimal solution. Use linear
A farm specializes in the production of a special cattle feed, which is a mixture of corn and soybeans. The nutritional composition of these ingredients and their costs are as follows:
- Corn contains 0.09 g of protein and 0.02 g of fiber per gram, with a cost of.
$0.30 per gram.
- Soybeans contain 0.60 g of protein and 0.06 g of fiber per gram, at a cost of $0.90 per gram.
0.90 per gram.
The dietary needs of the specialty food require a minimum of 30% protein and a maximum of 5% fiber. The farm wishes to determine the optimum ratios of corn and soybeans to produce a feed with minimal costs while maintaining nutritional constraints and ensuring that a minimum of 800 grams of feed is used daily.
Restrictions
1. The total amount of feed should be at least 800 grams per day.
2. The feed should contain at least 30% protein and no more than 5% fiber.
Objective
Minimize the total daily cost of the feed mix.
Questions
1. How many grams of corn and soybeans should be used to minimize the daily cost?
2. What is the minimum daily cost of the food mix?
to generate a solution
a solution
- I'm struggling to create 5 histograms for a set of data in Python. Conceptually the code should produce a histogram which gets smoother and closer to a normal curve as the number of values increases in the array. Mine are all plotting on the same graph however and are separated at weird values and I don't know why. They're not curve like at all. For reference, my 5 arrays of values are generated randomly from a normal distribution with a mean of 12, standard deviation of 1, and contain 5, 50, 500, 5000, and 5000000 values respectively.arrow_forwardIN PYTHON A tridiagonal matrix is one where the only nonzero elements are the ones on the main diagonal and the ones immediately above and below it.Write a function that solves a linear system whose coefficient matrix is tridiag- onal. In this case, Gauss elimination can be made much more efficient because most elements are already zero and don't need to be modified or added. As an example, consider a linear system Ax = b with 100,000 unknowns and the same number of equations. The coefficient matrix A is tridiagonal, with all elements on the main diagonal equal to 3 and all elements on the diagonals above and below it equal to 1. The vector of constant terms b contains all ones, except that the first and last elements are zero. You can use td to find that x1= −0.10557. The following code format should help: def td(l, m, u, b): '''Solve a linear system Ax = b where A is tridiagonal Inputs: l, lower diagonal of A, n-1 vector m, main diagonal of A, n vector u,…arrow_forwardGiven a vector of real numbers r = (r1, V2, ..., rn). We can standardize the vector using the formulation: V; = "im, where m is the ri-m mean of the vector r, and s is the standard deviation of r. The vector v = (V1, V2, . , Un) will be the scaled vector. ... 9 Write a Python function scale_vec(r) that takes the vector r as input and returns the scaled vector v. Sample inputs and outputs: • Input: np.array([1, 3, 5]), output: [-1.22474487 0. 1.22474487] Input: np.array([3.3, 1.2, -2.7, −0.6]), output: [1.35457092 0.40637128 -1.35457092 -0.40637128] Hint: Use numpy.mean and numpy.std with default parameters. [ ] # Write your function here Let's test your function. [ ] import numpy as np print (scale_vec(np.array ( [1, 3, 5]))) print (scale_vec(np.array([3.3, 1.2, -2.7, -0.6])))arrow_forward
- Using the C Programming language, write a program that sums an array of 50 elements. Next,optimize the code using loop unrolling. Loop unrolling is a program transformation that reduces thenumber of iterations for a loop by increasing the number of elements computed on each iteration.Generate a graph of performance improvement.arrow_forwardPart C: Function, for and plotting We did a project in the lecture on calculating the free fall speeds and plotting them on a graph. This part is similar to the project. An engineer has derived a relationship between the force applied to a material and the extension in length that the force would cause. The relationship between force f and extension e is given by: You are asked to plot a graph showing the relationship between force and extension. You are asked to complete the following tasks: Task 1 Write a Python function which returns the value of e for a given input f. Do not use literals (e.g. 5.5, 10) in the expressions for e in the function. Instead you should define constants and use them. Note that the relationship between e and f depends on whether f is bigger than 10 or not, this means you need a certain Python construction in your function. If you can't think of that, have a look at Part A of Lab03.arrow_forwardIN PYTHON I want a code in python and no need to post work on paper Iwant a screenshot of the code A tridiagonal matrix is one where the only nonzero elements are the ones on the main diagonal and the ones immediately above and below it.Write a function that solves a linear system whose coefficient matrix is tridiag- onal. In this case, Gauss elimination can be made much more efficient because most elements are already zero and don't need to be modified or added. As an example, consider a linear system Ax = b with 100,000 unknowns and the same number of equations. The coefficient matrix A is tridiagonal, with all elements on the main diagonal equal to 3 and all elements on the diagonals above and below it equal to 1. The vector of constant terms b contains all ones, except that the first and last elements are zero. You can use td to find that x1= −0.10557. The following code format should help: def td(l, m, u, b): '''Solve a linear system Ax = b where A is tridiagonal Inputs: l,…arrow_forward
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