Releases: ds4dm/ecole
Releases · ds4dm/ecole
v0.8.1
v0.8.0
New features
- Make
libecole
a installable CMake library (no Python). #240 - Refactor Ecole packaging to allow the creation of Ecole Python extensions. #282
- Add method to reactivate SCIP logger. #283
- Added possibility use SCIP default in
Branching
environment by passingecole.Default
#294 #303
Bug fixes
- Normalized edge coefficients in node bipartite observation. #270
- Fix difficulty of instance generators #301
Breaking changes
- Change StrongBranching
pseudo_candidate
parameter default value. #239 - Observations functions now use uniformly variable indices (instead of LP). In most cases, this will not impact user code. #210
- Remove deprecated functions/emum
NodeBipartiteObs.column_features
,NodeBipartiteObs.ColumnFeatures
- Rename
RandomEngine
toRandomGenerator
- Renamed exceptions to
ScipError
andMarkovError
#302 - Move to SCIP8 on Conda-forge #303
v0.7.3
Bug fixes
- Add constructor option to extract only LP branching candidates in
Kalild2016
(default behavior is changed to match that of theBranching
environment).
v0.7.2
v0.7.1
v0.7.0
New features
- Add a
FileGenerator
to iterate over local problem instances #174 - Add
Hutter2011
observation function #192 - Add
PrimalIntegral
,DualIntegral
,PrimalDualIntegral
reward functions #144 - Add
BranchingSum
multi-variable branching environment #181 - Add
PrimalSearch
environment #183 - Terminal states (
done==True
) always returnNone
as an observation #199 - Rewards are now extracted before observations, to account for the observation extraction cost between two rewards #212
Bug fixes
- Fixed a bug in
IndependentSetGenerator
where the greedy clique strengthening was not as greedy as it should. - Documentation typos
v0.6.2
v0.6.1
v0.6.0
New features
- Add MilpBipartite observation function for pre-solving (#147)
- Add Pickle support to all observation functions (#106 #152)
- Features description for
Khalil2016
(#157)
Breaking changes
NodeBipartite
features have been reordered- Changed combinatorial auctions, maximum independent set and capacitated facility location instance generators so that they produce instances of difficulty similar to those of Gasse et al. (2019) out-of-the-box (#141, #142 #150)
Khalil2016
now returns a struct wrapping the Numpy array (#157)