Optimization a program with 7 free parameters

In summary, the conversation discusses the difficulty of optimizing an expression with 7 free parameters and the suggested method of using nested for loops. It is mentioned that a systematic search in a 7-dimensional parameter space is impractical and other methods such as simulated annealing or evolutionary algorithms can be used to find the global minimum. The conversation ends with a suggestion to research these algorithms further through sources such as Wikipedia or books.
  • #1
sukharef
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Hello.
The problem is : I've got an expression to optimize with 7 free parameters. Ranges for each parameter are given. the simpliest way to optimize is to organize a program like for { for {for ...}} for each parameter and to get the result. Are there any other methods for solving the problem ?
Thank you!
 
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  • #2
Except for a few simple cases, a systematic search of a 7-dimensional parameter space is impossible in practice.

There are plenty of different methods to try to solve the problem. Assuming you want the global minimum, you can use for instance simulated annealing or evolutionary (genetic) algorithms.
 
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  • #3
DrClaude said:
Except for a few simple cases, a systematic search of a 7-dimensional parameter space is impossible in practice.

There are plenty of different methods to try to solve the problem. Assuming you want the global minimum, you can use for instance simulated annealing or evolutionary (genetic) algorithms.

could you give me some examples of those algorithms, please?
 
  • #4
sukharef said:
could you give me some examples of those algorithms, please?
Not without hours of work!

You can check Wikipedia or, much better, find a good book on the subject.
 
  • #5

FAQ: Optimization a program with 7 free parameters

1. What is optimization in programming?

Optimization in programming refers to the process of improving the performance and efficiency of a program by finding the best possible solution to a given problem. It involves adjusting various parameters and algorithms to achieve the desired outcome.

2. What are free parameters in programming?

Free parameters, also known as independent variables, are variables in a program that can be adjusted or changed without affecting other variables. In the context of optimization, these are the parameters that can be altered to improve the program's performance.

3. How many free parameters should be optimized in a program?

The number of free parameters that need to be optimized in a program can vary depending on the complexity of the problem and the desired outcome. In general, it is recommended to keep the number of free parameters as low as possible to avoid overfitting and improve the program's efficiency.

4. What are some techniques for optimizing a program with 7 free parameters?

There are various techniques that can be used to optimize a program with 7 free parameters, such as gradient descent, genetic algorithms, simulated annealing, and particle swarm optimization. Each technique has its own advantages and disadvantages, and the choice of which one to use depends on the specific problem and the available resources.

5. How can I evaluate the success of optimizing a program with 7 free parameters?

The success of optimizing a program with 7 free parameters can be evaluated by measuring the program's performance metrics, such as execution time, accuracy, and resource usage. It is also important to compare the optimized version with the original version of the program to determine the effectiveness of the optimization techniques used.

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