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st: RE: Optimization with constraints using Mata

From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   st: RE: Optimization with constraints using Mata
Date   Thu, 14 Aug 2008 17:16:08 +0100


Fit in terms of (say) q[1] = log p[1] and q[2] = log(p[2] + 1) and then
reverse after fitting. 

(Several recent threads have touched on this.) 

[email protected] 

Bernd Albrecht

I try to run a nonlinear optimization with constraints using Mata.

The initial settings for the optimization include the following function
specify the constraints:

optimize_init_constraints(S, real matrix Cc)  

where S=optimize_init() stores the default values.

The matrix Cc describes the constraints such that Cp'=c where p' is the
transpose of the parameter vector p.

(objective function: v=f(p) )

The problem I am facing refers to the equality sign in "Cp'=c".
the optimization function supports 

only equality constraints.

However, I try to solve a program with inequality constraints. My
constraints are pretty simple such as p[1]>0 

and p[2]>-1. 

Is there a way to get around this problem?

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