Does it make sense to scale bounds for 'lsqnonlin'?
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Hello,
I am wondering if I need to scale the initial guess vector as well as lower and upper bounds when dealing with MATLAB optimizers ("lsqnonlin" is of special interest). In other words, do the bounds have to be normalized, say, in the range [0 1]? Some of the unknown parameters in my optimization problem are several orders higher than the others so I do pre- and postmultiply them by certain numbers so that the ranges for all of the parameters are approximately equal. However, I would like to figure out if that is necessary at all. Does the "lsqnonlin" have a built-in scaling?
Thank you, Igor.
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Wu Wen
on 7 Apr 2017
Hi,
I'm doing something similar. I don't know exactly how the function 'lsqnonlin' works, but I'm gonna do some parametric study to investigate the sensitivity of the optimization to the scaling of the variables . I'm trying to make them close to the output value of the objective function. Please can you tell me if you solved this problem? Thank you.
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Wu Wen
on 12 Apr 2017
Hello,
I've done a series of optimizations with different scaling coefficients and I've got very different results. Basically you can control the size of the iteration step of the optimization process by using different scaling factors. If the steps are too small then the optimization does not progress much.
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