Supplying gradient function for some of the varibles

I'm using MaxLik for an optimization problem. The function that I want to optimize is pretty complex, so writting the gradient function for all parameters is not possible. However, for about half of the parameters I have been able to write the gradient function. So, my question is that could I supply these gradient functions to the optimization procedure, and for the rest of the parameters let the Maxlik itself calculate the gradients numerically? Because my job includes Monte Carlo simulations, right now each of my iterations takes about 15 minutes, and I hope to reduce it in this way.

Thank you in advance.

 

1 Answer



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accepted

The newer version of the application, Maxlikmt, does have the ability for you to specify a gradient procedure which computes numerical gradients for a subset of the procedures. I do not think that Maxlik has this ability. However, if you purchased Maxlik very recently you also received Maxlikmt as well.

The other option to consider is the Analytical Derivatives package. It can create a gradient procedure for you. You can then use this gradient procedure with your Maxlik program.

Finally, I recommend you contact Aptech technical support about your program. There are some excellent staff members who can probably help you speed up your code significantly.

aptech

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Your Answer

1 Answer

0
accepted

The newer version of the application, Maxlikmt, does have the ability for you to specify a gradient procedure which computes numerical gradients for a subset of the procedures. I do not think that Maxlik has this ability. However, if you purchased Maxlik very recently you also received Maxlikmt as well.

The other option to consider is the Analytical Derivatives package. It can create a gradient procedure for you. You can then use this gradient procedure with your Maxlik program.

Finally, I recommend you contact Aptech technical support about your program. There are some excellent staff members who can probably help you speed up your code significantly.


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