@@ -83,7 +83,8 @@ upperGR4J <- rep(+9.99, times = 4)
We start with a local optimization strategy by using the PORT routines (using the `nlminb()` of the `stats` package) and by setting a starting point in the transformed parameter space:
As can be seen below, the optimum performance criterion values (column *objective*) can differ from the global optimum value in many cases, resulting in various parameter sets.
```{r, warning=FALSE}
summary(parPORT)
summary(resPORT)
```
The existence of several local minima illustrates the importance of defining an appropriate starting point or of using a multi-start strategy or a global optimization strategy.
<!-- This is an expected result because the response surface for quadratic performance criteria of the **GR4J** model is generally sufficiently well defined in the transformed parameter space to allow using a local optimization strategy instead of a more time consuming global optimization strategy. -->