# Minimization to Maximization doubt in SVM

I came across a lecture on Support Vector Machines and in the lecture they converted a maximization problem into a minimization problem. I am wondering how it was done...

$Max \frac {1}{||x||}$

is converted into

$Min \frac{1}{2} x^Tx$

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I know nothing about VSM, but probably this was a constrained optimization problem where it is known that the solution is bounded away from $0$ (unconstrained it seems to make little sense). Recall that
$( ||x||)^2 = <x , x> = x^{T} x$.
Now, if $x$ is bounded away from 0, then maximizing $\frac{1}{|| x ||}$ is the same as minimizing $|| x ||$ (subject to the constraint I assume is missing; and maximizing the square root of a value leads to the same solution as maximizing the value). The factor $\frac{1}{2}$ is just for convenience to make the derivative prettier. After you calculate a solution with the factor, you back the solution w/o factor out from it.