Optimization with Constrained Function

In summary, the problem involves finding the dimensions for the minimum cost of fencing a 1000m^2 garden with 3 sides made of wooden fence and 1 side made of vinyl, which costs 5 times as much as wood. The length cannot be more than 30% greater than the width. After solving algebraically, it is suspected that the minimum cost is not necessarily when the vinyl side is minimal. The suggested solution is to solve the equation LW = 1000 for one variable, write the cost function as a function of that variable, and use calculus techniques to find the minimum cost while considering the constraint on the length.
  • #1
d=vt+1/2at^2
9
0

Homework Statement


1000m^2 garden. 3 sides made of wooden fence. 1 side made of vinyl(costs 5x as much as wood).

Length cannot be more than 30% greater than the width.

Find the dimensions for the minimum cost of the fence.



Homework Equations


1000 = LW
C = 2L + W + 5W


The Attempt at a Solution


Attempted ignoring the restriction. Answer does not meet restriction. Solved algebraically for the only rectangle where L = 1.3W and L = 1000/W. It is a calculus question and it is therefore suspected that this is not the answer. The minimum cost is not necessarily when the vinyl side is minimal.
 
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  • #2
I would start it this way: Solve the equation LW = 1000 for one variable, say W. Then write your cost function as a function of W alone. Use calculus techniques to find the minimum cost over the interval that includes all possible values of W, given the constraint that the length can't exceed 130% of the width.
 

1. What is optimization with constrained function?

Optimization with constrained function is a mathematical technique used to find the maximum or minimum value of a function subject to one or more constraints. The goal is to find the optimal solution that satisfies all the constraints.

2. Why is optimization with constrained function important?

Optimization with constrained function is important because it allows us to find the most efficient or best possible solution to a problem while taking into account any limitations or constraints. This can be applied to various fields such as engineering, economics, and operations research.

3. What are some common examples of optimization problems with constrained function?

Some common examples include maximizing profits while minimizing costs, finding the shortest path between two points while avoiding obstacles, and optimizing the design of a product while meeting certain constraints such as cost or size.

4. How is optimization with constrained function different from unconstrained optimization?

The main difference between the two is that constrained optimization takes into account constraints or limitations while searching for the optimal solution, whereas unconstrained optimization does not have any constraints. This means that the solution in constrained optimization must satisfy all the constraints, whereas in unconstrained optimization, the solution can be any point on the function.

5. What are some common techniques used in optimization with constrained function?

Some common techniques include linear programming, nonlinear programming, and dynamic programming. These techniques involve using mathematical models and algorithms to find the best solution that satisfies the given constraints.

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