Fastest known complexity for combinatorial ILP algorithm? I'm wondering, what is the best known algorithm, in terms of Big-$O$ notation, to solve Integer Linear Programming?

I know that the problem is $NP$-complete, so I'm not expecting anything polynomial. And I know there are lots of heuristics and such that are used in practical applications like CPLEX, but I'm more interested in the formal, worst-case complexity of an exact algorithm.

Some $NP$-complete problems have algorithms in time $O(b^n p(n))$ where $1 &lt; b &lt; 2$ and $p$ is a polynomial. Vertex cover, independent set and 3SAT fall into this category, but general-SAT and TSP don't (as far as we know).

Can any such statements be made about Integer Programming, or particular sub-instances?

If anyone has a reference for the related problem of Quantifier Free Presburger Arithmetic, I'd be very interested in that too.
From what I can tell by searching, the definite survey seems to be:

Aardal, Karen, Robert Weismantel, and Laurence A. Wolsey. "Non-standard approaches to integer programming." Discrete Applied Mathematics 123.1 (2002): 5-74.

In particular, Section 2.1 discusses integer programming in bounded dimension, and presents algorithms due to different authors. Indeed, the survey lists many references, and discusses some practical implementations.

For a fixed number of variables, integer linear programming is polynomial time solvable by Lenstra's algorithm.