Minimal set of inequalities

I have a set of $m$ linear inequalities in $R^n$, of the form $$A x \leq b$$ These are automatically generated from the specification of my problem. Many of them could be removed because they are implied by the others.

I would like to find the minimal set of inequalities, $$A' x \leq b'$$ such that a solution to the first problem is also a solution to the second, and viceversa.

One way to do this is to apply Fourier-Motzkin Elimination: I pick one inequality, $a_1 x \leq b_1$, I negate it as $-a_1 x < -b'$, I add it back to the system, and apply FME: if the result is an empty space, then the original inequality can be safely eliminated. Unfortunately, FME is a complex algorithm, and in the worst case it must be applied $m$ times.

Another possibility is to use linear programming: again, I remove one inequality $a_1 x \leq b$, and I call the remaining set of inequalities $A'' x \leq b''$. Then, I find an optimum for the problem $$\max \;\; a_1 x \\ A'' x \leq b''$$ using e.g. simplex, and finally I compare the result with $b$: if $a_1 x' < b$ then the inequality can be safely removed, otherwise it must be kept. Again, this has the same complexity of the simplex applied $m$ times (and $m$ is potentially very large).

Anyone knows a better algorithm?

-
Using Farkas' lemma, an halfspace $\{x \mid a' x \leq b' \}$ contains the polyhedron $P= \{x |A x \leq b \}$ if and only if there exists $\lambda \in \mathbb{R}^m$ with $\lambda \geq 0$ (understood component-wise) such that: \begin{align*} a' &= \lambda^T A \\ b' &\geq \lambda^T b \end{align*} The set \begin{align*} N= \{ (a',b') | \exists \lambda \geq 0, a' = \lambda^T A, b' \geq \lambda^T b \} \end{align*} is called the polar of the polyhedron $P$. The facets of $P$ are given by the extreme rays of $N$.
An inequality $a_i x \leq b_i$ will be non-redundant (i.e is not implied by the other) if and only if it is an extreme ray of $N$.
So all you need to do is to compute the conic hull of the $m$ points $(a_1,b_1),...,(a_m,b_m)$ which is more or less equivalent (some gap to fill here) to computing a convex hull. Convex hulls can be computed in $O(m^2)$ using quickhull http://www.cse.unsw.edu.au/~lambert/java/3d/quickhull.html or any other convex hull algorithm.