By good, I mean either the algorithm provides a relatively tight bound or it has a relatively fast running time. Any reference is welcome.
What are good approximation algorithms for the subset sum problem so far?
ds.algorithms;np hardness;approximation algorithms;approximation hardness;subset sum
Kellerer et al. (1997) gives with accuracy $\epsilon$ a $O(\min \{ n/ \epsilon, n + 1/ \epsilon^2 \log(1/ \epsilon) \})$ time and $O(n + 1/ \epsilon)$ space approximation scheme.Further improving on this, Kellerer et al. (2003) gives a FPTAS with $O(\min \{n \cdot 1/ \epsilon , n + 1/ \epsilon^2 \log( 1/ \epsilon) \} )$ time and $O(n+1/ \epsilon)$ space. In addition, answering your question on relatively fast running time, they noted that (based on computational results) that the scheme efficiently solves instances with up to $5000$ items with a guaranteed relative error smaller than $1/1000$.I am unsure if there are any newer results. As noted, because subset sum is a special case of the knapsack problem, one will probably find even more results when searching for that.UPDATE: You might also want to take a look at The Design of Approximation Algorithms, Williamson and Shmoys, 2011, see the chapter starting at page 65 about the Knapsack problem. They give a FPTAS (on page 68) for the Knapsack problem that runs in $O(n^3/\epsilon)$ time. It might be that the algorithms specifically designed for the Subset sum problem are faster than the more general ones for the Knapsack.