Numerical Technique to approach Global Minimum of a Function
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I have a function which has 15 input parameters and outputs the Mean Square error of a curvefit. My aim is to find the 15 parameter values whose combination outputs a value close to zero (I'm hoping for 10^(-4)). I have tried implementing Gradient Descent Method, Levenberg-Marquardt algorithm (lsqnonlin) and even solve command. They appear to depend heavily on initial guesses and settle for a local minimum. I'm hoping someone could guide me towards a suitable technique for global minimum, and its implementation. Please let me know if you need more details. Thanks in advance.
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Matt J
2022 年 12 月 4 日
編集済み: Matt J
2022 年 12 月 4 日
They appear to depend heavily on initial guesses and settle for a local minimum.
All methods depend heavily on initial guesses, in general. The question you need to ask is how, for your specific model, do you generate a good initial guess. The answer to that requires us to see the model.
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Kartik
2023 年 3 月 21 日
Hi,
It sounds like you're dealing with a highly nonlinear optimization problem with many variables, which can be challenging to solve using standard optimization methods. To find a global minimum, you may want to consider using a stochastic optimization algorithm, such as genetic algorithms or particle swarm optimization. These methods are designed to search a large solution space efficiently and can often find global optima.
You can refer the following MathWorks documentation for information regarding PSO in MATLAB:
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