On Dec 6, 2010, at 15:15 , Jonathan P Daily wrote:
Correct me if I'm wrong, but isn't the minimal x value in your example
the same regardless of what positive coefficient you apply to x? If that
is the case, you would expect the same min(x) for each iteration.
i.e. in the interval [0,1] the minimum x value of x^2 + x is the same as
x^2 + 100000000*x, at x = 0.
You're wrong --- slightly. The returned $minimum is the x, the y is
$objective. But the interval given doesn't bracket the minimum, as you'll
clearly see if you put int=c(-10,10). The only puzzling bit is that
optimize() doesn't actually return the left endpoint, but rather the first
evaluation point inside the interval. The rather wide tolerance of
.Machine$double.eps^0.25 == 0.0001220703 probably plays a role in this.