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From |
Alberto Dorantes <alberto.dorantes@finanzastec.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Re: optimize with unequality constraints |

Date |
Thu, 19 Apr 2012 11:30:48 -0500 |

For those who might be interested in my "optimize" problem, I found a solution to include unequal constraints using optimize in Mata. Here is an example that solve my problem based on the code I had posted void retorno_ajustado(real scalar todo, real vector p, real vector R, real matrix COV, ret, grad, Hess) { real matrix q q=J(1,4,0) // I create a temporal matrix q, that will be a log transformation of the vector p I want to solve. This helps me to add the constraints // that all p's will be greater than zero, and I can also add the constraint that a specific p can be greater to any point between // 0 and 1. In the code I included the constraints that p[1]>0.10, and p[4]>0.20: q[1,1]=ln(p[1]-0.1) q[1,2]=ln(p[2]) q[1,3]=ln(p[3]) q[1,4]=ln(p[4]-0.2) // Previous code I had was: // ret= p*R' / sqrt(p*COV*p') // Here is the same, but more efficient, and I added the transformed q variables without changing my objective function value: ret=quadcross(p',R') / sqrt(quadcross(p',COV)*p') + q[1,1] - q[1,1] + q[1,2] - q[1,2] + q[1,3] - q[1,3] + q[1,4] - q[1,4] } S = optimize_init() optimize_init_evaluator(S, &retorno_ajustado()) optimize_init_evaluatortype(S, "d0") optimize_init_params(S,(0,1)) C=J(1,4,1) c=(1) COV=st_matrix("COVA") R = [0.28, 0.19, 0.05, 0.42] optimize_init_argument(S,1,R) optimize_init_argument(S,2,COV) optimize_init_constraints(S,(C,c)) optimize(S) optimize_result_params(S) optimize_result_value(S) retorno=p*R' retorno varianza=p*COV*p' desv=sqrt(varianza) desv p end //(COVA is a Var-Cov matrix of 4x4) Regards... Alberto. 2012/4/1 Alberto Dorantes <alberto.dorantes@finanzastec.net>: > Hi all. > Is there a way to specify unequality constraints to the optimize function > in Mata? > I have f(p) as a function to maximize. I found a way to put an > equality constraint > (the sum of p[i] is equal to one), but I could not specify the non-negative > constraint to each p[i] (p[i]>0). > > I read in Statalist that using logs can solve the problem, but I have not > figure out how to transform my p vector to logs since I', afraid that my > function will not work correctly. > > To be more specific, here is my code: > > mata: > void retorno_ajustado(real scalar todo, real vector p, > real vector R, real matrix COV, > ret, grad, Hess) > { > ret= p*R' / sqrt(p*COV*p') > } > > S = optimize_init() > optimize_init_evaluator(S, &retorno_ajustado()) > optimize_init_evaluatortype(S, "d0") > optimize_init_params(S,(0,1)) > C=J(1,2,1) > c=(1) > COV=st_matrix("COVA") > R = [0.28, 0.19] > optimize_init_argument(S,1,R) > optimize_init_argument(S,2,COV) > optimize_init_constraints(S,(C,c)) > optimize(S) > optimize_result_params(S) > optimize_result_value(S) > end > > (COVA is a Var-Cov matrix of 2x2) > > Thanks in advance! > > Carlos A. Dorantes * * 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: optimize with unequality constraints***From:*Alberto Dorantes <alberto.dorantes@finanzastec.net>

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