in this article we present a method that combines maximum likelihood estima
tion and nonlinear programming in growth modeling. The method of Hooke and
Jeeves is used to discover the optimal specification of a particular compet
ition index type, while statistical software is used to fit the regression
model with the given competition index type, The log-likelihood computed by
the statistical software is fed back to the optimization algorithm, which
alters the specification of the competition index type based on the changes
in the log-likelihood. This approach was tested for a mixture of Scots pin
e (Pinus sylvestris L,) and Norway spruce (Picea abies [L.] Karst,). The ch
aracteristics of five different competition index types were optimized. The
best model included an index computed from vertical angles formed by a hor
izontal plane and the tops of competitors. The elevation of the horizontal
plane was computed with a species-specific linear regression model using he
ight of the subject tree as the predictor. Pine competitors nearer than 6 m
and spruce competitors nearer than 9-10 m were included in the optimal com
petition index. This study showed that the approach used here is highly eff
icient.