How to minimise this function and get initial conditions

In summary, in order to minimize the given function and obtain the initial conditions, one must find the minimum value at a vertex of the rectangular solid defined by the given constraints. The given values of v= 144.2, f= 0.025, y= 9.5 do not correspond to a minimum value as they do not align with any vertex.
  • #1
Bharatisha
1
0
How to minimise this function and get initial conditions . I have the answer for initial conditions.
ra = min(00237 − 0000175v + 8.693f − 000159y)
subjected to

124.53 ≤ v ≤ 167.03
0.025 ≤ f ≤ 0.083
6.2 ≤ y ≤ 14.8

v= 144.2 , f = 0.025, y = 9.5 How to get this?
 
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  • #2
Bharatisha said:
How to minimise this function and get initial conditions . I have the answer for initial conditions.
ra = min(00237 − 0000175v + 8.693f − 000159y)
subjected to

124.53 ≤ v ≤ 167.03
0.025 ≤ f ≤ 0.083
6.2 ≤ y ≤ 14.8

v= 144.2 , f = 0.025, y = 9.5 How to get this?
You can't get this, it is NOT the correct answer.

This is a linear function, defined on a convex set (a rectangular solid, actually). Different values of "ra" represent different planes parallel to one another. If the plane corresponding to a given value of ra passes inside the rectangular solid, we can decrease ra slightly by moving the plane parallel to itself. This is true until the plane passes outside the rectangular solid. It little thought should show you that the last point of the rectangular solid the planes touches will be a vertex (or and entire edge if two vertices give the same value).

So a minimum value for a linear function on a polygonal solve (such as a rectangular solid) must occur at a vertex! Here, it is easy to see that "v= 144.2, f= 0.025, y= 9.5" does NOT give a minimum value because no vertex has "v= 144.2" or "y= 9.5".

(your given function is "00237 − 0000175v + 8.693f − 000159y". What do those coefficients without a decimal point mean? Is "00237" just 237 or .00237 or 0.0237?)
 
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FAQ: How to minimise this function and get initial conditions

1. How do I define the function that I want to minimize?

To define the function, you need to have a clear understanding of the problem you are trying to solve. This includes identifying the variables and parameters involved, and how they relate to each other. Once you have a clear understanding of the problem, you can then write an equation that represents the function you want to minimize.

2. What is the significance of finding the initial conditions?

The initial conditions represent the starting point for the minimization process. They determine the values of the variables at the beginning of the optimization and can greatly impact the final result. Finding the right initial conditions is crucial for effectively minimizing a function.

3. What methods can I use to minimize a function?

There are several methods for minimizing a function, including gradient descent, Newton's method, and simulated annealing. The most suitable method depends on the specific function and problem at hand. It is important to research and understand the different methods before deciding on the most appropriate one for your problem.

4. How do I know if I have successfully minimized the function?

The success of the minimization process can be evaluated by checking if the function has reached a minimum value or if the values of the variables have converged to a stable solution. Additionally, you can compare the results to known optimal values or use numerical methods to verify the accuracy of the solution.

5. Can I use machine learning techniques to minimize a function?

Yes, machine learning techniques such as neural networks and genetic algorithms can be used for function minimization. These methods use iterative processes and optimization algorithms to find the optimal solution. However, it is important to have a good understanding of the problem and the limitations of these techniques before using them for minimization.

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