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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Optimization with constraints using Mata |

Date |
Thu, 14 Aug 2008 17:16:08 +0100 |

Re-parameterise. 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.) Nick n.j.cox@durham.ac.uk Bernd Albrecht I try to run a nonlinear optimization with constraints using Mata. The initial settings for the optimization include the following function to 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". Apparently, 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? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Optimization with constraints using Mata***From:*"Bernd Albrecht" <albrecht@corporate-finance-mannheim.de>

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