Trieste

**New functionality**

New acquisition functions:
- AugmentedExpectedImprovement (265)
- GIBBON (275)
- ExpectedConstrainedHypervolumeImprovement (285)
- BatchMonteCarloExpectedHypervolumeImprovement (257)

New samplers:
- RandomFourierFeatureThompsonSampler (266)
- approximate (feature-based) Thompson sampling (274)

**Improvements**

Better model fitting:
- GPR kernel initialization (277)
- BayesianOptimizer initial model fit (283)
- Support model-specific optimization parameters (287)
- Including kernel prior term in the likelihood when choosing kernel params (290, 291)
- Sample from constrained kernel parameters before model fitting (297, 303, 305)

Better acquisition optimization:
- Better error handling in continuous acquisition optimizer (289, 313)
- Better continuous optimizers with L-BFGS-B support (276) and recovery restarts (313)

Experimental design support for continuous search spaces through Sobol/Halton (259)

ExpectedConstrainedImprovement efficiency improvement (284)
Better handling of tf.function (299, 309)
Objective functions moved to a separate package, added search space variables (302)
Better numerical stability in GIBBON/MES (310)

**Build changes**

More notebook documentation (280, 288, 310)
Improved instructions for contributions and discussions (301)

This point release updates the [GPflow](www.gpflow.org) dependency to version 2.2. It adds no new features.

**New functionality**

add support for multi-objective optimization with the expected hypervolume improvement acquisition function (177) (194) (202) (207) (217) (225) (243)
add support for batch optimization via local penalization (230) (251)
allow custom acquisition function optimizers (186)
add various toy objective functions: Gramacy & Lee (168), Goldstein-Price (169), VLMOP2, DTLZ (190), Hartmann (204), Rosenbrock, Ackley (241), Shekel (250)

**Improvements**

simplify single model/dataset use case (252)
expose predict_y from GPFlow models (254)
support arbitrary tensor-likes as inputs, not just lists (234)
improve and track unit test code coverage (222) (236)

**Build changes**

simplify docs build and add it to build checks (231) (240)
add taskipy support for running tests (219) (244)

**New functionality**

add Monte-Carlo-based sampler for joint distributions, using reparametrization trick (93)
add Monte-Carlo-based batch Expected Improvement acquisition function (133)
add tutorials for batch-sequential acquisition functions (149) (151)
add `predict_joint` method to root model interface `ProbabilisticModel` for predicting the mean and variance of joint distributions (93)
support lists as lower and upper bound arguments to `Box` (112)
add py.typed so that trieste type hints can be used by client code (140)
add efficient `astuple` conversion method on `Dataset` (106)
add support for optimizing all GPflow model wrappers with either `tf.optimizers.Optimizer`s (with or without mini-batching) or `gpflow.optimizers.Scipy` (47)

**Improvements**

significant refactor of `BayesianOptimizer` return type, to reduce the chance of working with the result of incomplete BO runs (17)
merge equivalent tensor type aliases (those in `type` module) (76)
deepcopying is optimized on types typically copied while tracking state in `BayesianOptimizer` (104)
fix type inconsistency in `VariationalGaussianProcess`'s constructor (116)

**Build changes**

various improvements to documentation site, including "how-to" section in tutorials (63) and formatting for bibtex references (110)
add flake8 code linter (109) and isort import organiser (107) to build checks
add missing build dependencies to pyproject.toml (141)

**Improvements**

add missing imports to acquisition function functionality (100)

**New functionality**

add Monte-Carlo samplers (and corresponding acquisition function builders) for the reparametrization trick with (non-batch) acquisition functions (94) (95)
add an acquisition rule for batches of points (69)
add expected constrained improvement acquisition function (9)
add a static probabilistic model interface (23)
add method for Cartesian product of search spaces (68)

**Improvements**

rename `datasets` module to `data` (10)
add unit tests (62) (60) (59) (58) (44) (31)
improve VGP update efficiency and stability (46)
make `BayesianOptimizer` attributes `observer` and `search_space` private (15)
update Slack invitation link (71)
fix VGP model in notebook "EGO with a failure region" (45)

**Build changes**

remove master branch (22)
introduce black formatter (20)
include doctests in CI run (19)
update CI to use new pip dependency resolver (78)