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Thread Subject:
nonlinear optimization

Subject: nonlinear optimization

From: yunzhi cheng

Date: 17 Jan, 2008 23:42:02

Message: 1 of 5

I wonder if there are any intelligent methods to optimize
nonlinear function.
I know some of methods such as Fuzzy, ANN, GA. The problem
is that it cost much time to evaluate the function. So I
just want to a method which require less steps to get the
solution even it is not good enough. It can converge fast
even the solution is not global optimization.
 

Subject: nonlinear optimization

From: cjkogan111

Date: 18 Jan, 2008 00:14:49

Message: 2 of 5

Have you looked at the MATLAB functions: lsqnonlin and fminsearch?
Hope this helps,
-cjkogan111

Subject: nonlinear optimization

From: Marcus M. Edvall

Date: 18 Jan, 2008 02:57:46

Message: 3 of 5

TOMLAB /CGO is specifically designed for this: http://tomopt.com/tomlab/products/cgo/

Best wishes, Marcus
Tomlab Optimization Inc.
http://tomopt.com/tomlab/

Subject: nonlinear optimization

From: yunzhi cheng

Date: 1 Feb, 2008 22:35:03

Message: 4 of 5

Thanks.
But what can do if I need write my own programming.

Subject: nonlinear optimization

From: roberson@ibd.nrc-cnrc.gc.ca (Walter Roberson)

Date: 1 Feb, 2008 22:57:39

Message: 5 of 5

In article <fmop4a$89l$1@fred.mathworks.com>,
yunzhi cheng <sjtu_yh@yahoo.com> wrote:
>I wonder if there are any intelligent methods to optimize
>nonlinear function.
>I know some of methods such as Fuzzy, ANN, GA. The problem
>is that it cost much time to evaluate the function. So I
>just want to a method which require less steps to get the
>solution even it is not good enough. It can converge fast
>even the solution is not global optimization.

Pick a random point within the solution space. Evaluate the
function at that location. Declare the point to be the
local minima point and the evaluation result to be the local
minima value, and exit the "solver". Convergance of this
"solver" is perfect within the allowed tolerance.

If the above does not appear to be a suitable solution, then
you may need to tell us how you would judge the quality of
any particular method. Is a method acceptable if it quickly
returns the exact bottom of a small dent that is on the top of
a large spike?

Generally speaking, the more initial information you have about
the shape of the surface, the better you can choose which
method to use. For example, is the surface continuously
differentiable? Does its first derivative exist everywhere, and
do you have the derivative available as a function that can be
evaluated? Does the function have many local minima or only
a few? And so on.
--
  "Is there any thing whereof it may be said, See, this is new? It hath
  been already of old time, which was before us." -- Ecclesiastes

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