Speedups Achieved
Although ParamILS can in
principle optimize arbitrary user-defined objective functions, so far, it has achieved its
greatest successes in minimizing the runtime algorithms require to solve a given problem.
For doing so effectively, ParamILS relies on the idea of
``adaptive capping'', introduced in the ParamILS JAIR article.
| Problem Domain |
Algorithm(s) |
Speedups
over default |
Citation |
| Propositional
Satisfiability |
SPEAR |
4.5-500 |
[Hutter at al., FMCAD
'07] |
| SAPS |
8.1-130 |
[Hutter et al., AAAI '07] |
| SATENSTEIN |
1.6-2.8 |
[KhudaBukhsh et
al, IJCAI '09] |
| Captain Jack |
|
|
| Clasp |
|
|
| Lingeling |
|
|
| PicoSAT |
|
|
| Mixed
Integer Programming |
CPLEX |
2.0-52 |
[Hutter et al.,
CPAIOR '10] |
| GUROBI |
1.2-2.3 |
[Hutter et al.,
CPAIOR '10] |
| LPSOLVE 1.0 |
1.0-153 |
[Hutter et al.,
CPAIOR '10] |
| Timetabling |
UBCTT |
>28 |
[Fawcett et al.,
Tech Report '09] |
| AI
Planning |
FASTDOWNWARD |
1.0-23 |
[Fawcett et al.,
ICAPS-PAL
'11] |
| LPG |
3.0-118 |
[Vallati et al.,
ICAPS-PAL '11] |
| Most
probable explanation |
GLS+ |
>360 |
[Hutter et al., AAAI '07] |
| Protein
folding |
REMC |
1.2-3.0 |
[Thachuk et al.,
BMC Bioinformatics '07] |