Newton's Method of Nonlinear Minimization

Newton's method [163],[167, p. 143]
finds the minimum of a nonlinear (scalar) function of several
variables by locally approximating the function by a quadratic
surface, and then stepping to the bottom of that ``bowl'', which
generally requires a matrix inversion. Newton's method therefore
requires the function to be ``close to quadratic'', and its
effectiveness is directly tied to the accuracy of that assumption.
For smooth functions, Newton's method gives very rapid *quadratic
convergence* in the last stages of iteration. Quadratic convergence
implies, for example, that the number of significant digits in the
minimizer approximately doubles each iteration.

Newton's method may be derived as follows: Suppose we wish to minimize the real, positive function with respect to . The vector Taylor expansion [550] of about gives

for some , where . It is now necessary to assume that . Differentiating with respect to , where is presumed to be minimized, this becomes

Solving for yields

Applying Eq.(7.14) iteratively, we obtain Newton's method:

where is given as an initial condition.

When the
is any *quadratic form* in
, then
, and
Newton's method produces
in one iteration; that is,
for every
.

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