Unconstrained optimization on the plane: necessary condition
See a very simple geometric discussion of the one-dimensional case. It reveals the Taylor decomposition as the main research tool. Therefore we give the Taylor decomposition in a 2D case. Assuming that the reader has familiarized him/herself with that information, we go directly to the decomposition
Here is a twice-differentiable function, is an internal point of the domain , is a small vector such that also belongs to the domain, is a row vector of first derivatives, and
is the Hessian (a matrix of second-order derivatives). stands for transposition.
When there is no local minimum or maximum?
We have seen how reduction to a 1D case can be used to study a 2D case. A similar trick is applied here. Let us represent the vector as where is another vector (to be defined later) and is a small real parameter. Then will be close to . From (1) we get
We think of as fixed, so the two expressions in square brackets are fixed numbers. Denote . An important observation is that
When tends to zero, tends to zero even faster.
Therefore the last term in (2) is smaller than the second, and from (2) we obtain
The no-extremes case. Suppose the vector of first derivatives is not zero: , which means that
(4) at least one of the numbers is different from zero.
Select . Then (3) implies
From (4) it follows that . Then (5) shows that cannot be an extreme point. Indeed, for small positive we have and for small negative we have . In any neighborhood of the values of can be both higher and lower than
Conclusion. In case (4) cannot be a local minimum or maximum. In other words, we should look for local extrema among critical points which satisfy the first order condition
The FOC is necessary for a function to have a local minimum or maximum. All of the above easily generalizes to dimensions higher than 2.