Does it make sense to scale bounds for 'lsqnonlin'?
4 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
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.
0 comentarios
Respuestas (1)
Wu Wen
el 7 de Abr. de 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.
2 comentarios
Wu Wen
el 12 de Abr. de 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.
Ver también
Categorías
Más información sobre Get Started with Optimization Toolbox en Help Center y File Exchange.
Productos
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!