Releases: eyounx/ZOOpt
Releases · eyounx/ZOOpt
v0.4
- Add
Dimension2
class, which provides another format to construct dimensions. Unlike Dimension class, Dimension2 allows users to specify optimization precision. - Add
SRacosTune
class, which is used to suggest/provide trials and process results for Tune (a platform based on RAY for distributed model selection and training).
v0.3
- Add a parallel implementation of SRACOS, which accelarates the optimization by asynchronous parallelization.
- Add a function that enables users to set a customized stop criteria for the optimization.
v0.2.3
v0.2.3
v0.2.2
- Fix bugs in the initialization step during the discrete optimization
- Fix the bugs that index_set size equals zero during the discrete
optimization - Now users can initialize sample set using
parameter = Parameter(..., init_samples = [Solution(), …], ...)
v0.2.1
- Fix bugs in the installation on Windows.
- Improve ZOOpt's interaction with users.
v0.2
- Add the noise handling strategies Re-sampling and Value Suppression (AAAI'18), and the subset selection method with noise handling PONSS (NIPS'17)
- Add high-dimensionality handling method Sequential Random Embedding (IJCAI'16)
- Rewrite Pareto optimization method. Bugs fixed.
v0.1
- Include the general optimization method RACOS (AAAI'16) and Sequential RACOS (AAAI'17), and the subset selection method POSS (NIPS'15).
- The algorithm selection is automatic. See examples in the example fold. -Default parameters work well on many problems, while parameters are fully controllable
- Running speed optmized for Python