Optimization with three variables

In summary: So the usual technique is to find the vertices (also called "extreme points") and evaluate the function at each of them. In this case, the feasible region is bounded by planes in 3-D space, and has the 4 vertices (0,0,250), (0,400/7,100), (250,0,0), and (250/3,200/3,0). So you need to evaluate C(X,Y,Z) at these 4 points and see which is smallest.In summary, the problem at hand is a linear programming problem where the goal is to find the cheapest method of preparing a 17% salt solution using three different stock solutions with varying costs. The relevant equations
  • #1
velocityviper
2
0

Homework Statement



You are a lab technician and must create 250 ml of a 17% solution. You have availible three stock solutions. You have a one liter container of a 5% salt, a 500 l contained of a 28% salt solution, and a 400 ml container of a 40% salt solution. Show the work necessary to calculate the cheapest ethod of preparing the 17% salt solution ifthe 5% solution costs $28 per liter, the 28% soltution costs $38 per liter, and the 40% solution costs $50 per liter.

So far I have this

2. Relavant equations

X+Y+Z=250
.05X+.28Y+.4Z=.17(250)
C(X,Y,Z)=(1/1000)(28X+38Y+50Z)

The Attempt at a Solution




I think I need a fourth equation so I can find the dervative and solve for 1 of the variables to optimize the solution,or I need to set derivative of the third equation = to 0, but I wouldn't know what to do from there.
 
Physics news on Phys.org
  • #2
velocityviper said:

Homework Statement



You are a lab technician and must create 250 ml of a 17% solution. You have availible three stock solutions. You have a one liter container of a 5% salt, a 500 l contained of a 28% salt solution, and a 400 ml container of a 40% salt solution. Show the work necessary to calculate the cheapest ethod of preparing the 17% salt solution ifthe 5% solution costs $28 per liter, the 28% soltution costs $38 per liter, and the 40% solution costs $50 per liter.

So far I have this

2. Relavant equations

X+Y+Z=250
.05X+.28Y+.4Z=.17(250)
C(X,Y,Z)=(1/1000)(28X+38Y+50Z)

The Attempt at a Solution




I think I need a fourth equation so I can find the dervative and solve for 1 of the variables to optimize the solution,or I need to set derivative of the third equation = to 0, but I wouldn't know what to do from there.


What about upper and lower bounds on X, Y and Z? (Can you use a negative amount of 5% solution? Can you use more than 1 L of 5% solution?)

Forget about setting derivatives to zero. In problems like this, the optimal solution occurs at a boundary, where some derivatives are non-zero. Your problem is called a LINEAR PROGRAMMING problem, and is not solvable by calculus methods. See, eg., http://www.purplemath.com/modules/linprog5.htm .

RGV
 
  • #3
Are any derivatives needed at all? Or is it just using systems of equations to slove for one of the variables and then using that to solve for the others?
 
  • #4
velocityviper said:
Are any derivatives needed at all? Or is it just using systems of equations to slove for one of the variables and then using that to solve for the others?

Well, the problem involves both equalities and inequallities. Let me explain what I mean in an extended example. For example, suppose we have the problem
maximize 5x + 3y
subject to
2x + y <= 50
3x + 5y <= 200
y <=10
x >=2, and
x,y >= 0.
We must first convert to equalities and pure sign constraints; in other words, re-write the problem so that all inequalities have the form 'variable >= 0'. We do this by adding a so-called slack variable to the left-hand-side (LHS) of a <= constraint, to bring the LHS up to the right-hand-side (RHS), and by subtracting a so-called surplus variable from the LHS of a >= constraint (excluding non-negativity), to bring the LHS down to the RHS. So, we have slack variables s1,s2 and s3 for constraints (1)-(3) and a surplus variable s4 for constraint (4). Now the problem becomes:
max Z1 = 5x + 3y
subject to
2x + y + s1 = 50
3x + 5y + s2 = 200
y + s3 = 10
x - s4 = 2,
x,y,s1,s2,s3,s4 >= 0.

Now the only inequalities are of the form "var >= 0"; all other restrictions are *equations*.
We have 4 equations in the 6 unknowns (x,y,s1,s2,s3,s4).

There are algorithms available to solve such problems efficiently, but none of them involve calculus---just linear algebra. Essentially, we can pick 4 of the 6 variables to regard as "dependent" and solve for them as functions of the other 2 "independent" variables. If we pick the right independent variables then the solution of the problem will become "obvious" (when written the right way), and there are systematic algorithms to move from in a finite number of steps from one choice of independent variables to a better choice, until we have found the optimal choice. I won't say more about this now. Be assured, however, that some industrial-scale linear programming problems can involve hundreds of thousands to a few million constraints and several million variables, and these are solved by companies quite often in model-aided decision-making.

RGV
 
  • #5
No, no derivatives are needed. This is a "linear programming" problem. With two or three variables, it can be done geometrically. The basic result of linear programming is that a linear function, with linear constraints, so that the set of points satisying those constraints (the "feasible" region) forms a convex polytope, has its maximum and minimum values at one of the vertices.
 

Related to Optimization with three variables

1. What is optimization with three variables?

Optimization with three variables is a mathematical process of finding the maximum or minimum value of a function with three independent variables, subject to certain constraints.

2. What are the common techniques used for optimization with three variables?

Some common techniques used for optimization with three variables include the simplex method, the gradient descent method, and the Lagrange multiplier method.

3. How is optimization with three variables useful in science?

Optimization with three variables is useful in science, particularly in fields such as physics, engineering, and economics, as it allows us to find the most efficient solutions to complex problems by considering multiple variables and constraints.

4. What are the limitations of optimization with three variables?

One limitation of optimization with three variables is that it can become computationally intensive for complex functions and large datasets. Additionally, it assumes that the function being optimized is continuous and differentiable.

5. Can optimization with three variables be applied to real-world problems?

Yes, optimization with three variables can be applied to real-world problems such as resource allocation, production planning, and portfolio optimization. It can also be used in machine learning and data analysis to find the best model parameters for a given dataset.

Similar threads

  • Calculus and Beyond Homework Help
Replies
5
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
2K
  • Biology and Chemistry Homework Help
Replies
1
Views
2K
Replies
6
Views
2K
  • Calculus and Beyond Homework Help
Replies
11
Views
3K
  • Precalculus Mathematics Homework Help
Replies
13
Views
4K
  • Introductory Physics Homework Help
Replies
2
Views
4K
  • Precalculus Mathematics Homework Help
Replies
2
Views
3K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
3K
Back
Top