I notice that at 100 % growth and 0 % benefit, B ( t ) = 0 for all t, so NO benefit accumulation occurs, so clearly 100 % growth is not an optimum point, and 99 % growth will give you more benefit at t = infinity.
12.
BTW, since the global optimum points searching is still an important open problem ( simulated annealing or genetic algorithms dont guarantee anything ), does this mean that the problems of making efficient integration algorithm is also still open ?-- 131.111.164.219 12 : 27, 6 September 2006 ( UTC)
13.
By comparison, prediction in frequentist statistics often involves finding an optimum point estimate of the parameter ( s ) e . g ., by maximum likelihood or maximum a posteriori estimation ( MAP ) and then plugging this estimate into the formula for the distribution of a data point.
14.
It becomes clear that, at the optimum point for the individual acting independently described in the last paragraph the collective good is provided if F _ i > \ frac { C } { V _ g }, for if F _ i = \ frac { V _ i } { V _ g }, then V _ i > C.
