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Changelog for 0.9.0 (#1959)
Summary: ## Motivation Adding to the changelog so we can put out a new release ### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)? Yes Pull Request resolved: #1959 Test Plan: None really Reviewed By: saitcakmak Differential Revision: D47941897 Pulled By: esantorella fbshipit-source-id: bb15e009eef28b4e7dd3a572dc95e0073d797f59
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CHANGELOG.md

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The release log for BoTorch.
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## [0.9.0] - July 31, 2023
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#### Compatibility
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* Require Python >= 3.9.0 (#1924).
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* Require PyTorch >= 1.13.1 (#1960).
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* Require linear_operator == 0.5.0 (#1961).
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* Require GPyTorch == 1.11 (#1961).
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#### Highlights
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* Introduce `OrthogonalAdditiveKernel` (#1869).
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* Speed up LCE-A kernel by over an order of magnitude (#1910).
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* Introduce `optimize_acqf_homotopy`, for optimizing acquisition functions with homotopy (#1915).
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* Introduce `PriorGuidedAcquisitionFunction` (PiBO) (#1920).
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* Introduce `qLogExpectedImprovement`, which provides more accurate numerics than `qExpectedImprovement` and can lead to significant optimization improvements (#1936).
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* Similarly, introduce `qLogNoisyExpectedImprovement`, which is analogous to `qNoisyExpectedImprovement` (#1937).
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#### New Features
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* Add constrained synthetic test functions `PressureVesselDesign`, `WeldedBeam`, `SpeedReducer`, and `TensionCompressionString` (#1832).
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* Support decoupled fantasization (#1853) and decoupled evaluations in cost-aware utilities (#1949).
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* Add `PairwiseBayesianActiveLearningByDisagreement`, an active learning acquisition function for PBO and BOPE (#1855).
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* Support custom mean and likelihood in `MultiTaskGP` (#1909).
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* Enable candidate generation (via `optimize_acqf`) with both `non_linear_constraints` and `fixed_features` (#1912).
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* Introduce `L0PenaltyApproxObjective` to support L0 regularization (#1916).
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* Enable batching in `PriorGuidedAcquisitionFunction` (#1925).
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#### Other changes
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* Deprecate `FixedNoiseMultiTaskGP`; allow `train_Yvar` optionally in `MultiTaskGP` (#1818).
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* Implement `load_state_dict` for SAAS multi-task GP (#1825).
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* Improvements to `LinearEllipticalSliceSampler` (#1859, #1878, #1879, #1883).
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* Allow passing in task features as part of X in MTGP.posterior (#1868).
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* Improve numerical stability of log densities in pairwise GPs (#1919).
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* Python 3.11 compliance (#1927).
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* Enable using constraints with `SampleReducingMCAcquisitionFunction`s when using `input_constructor`s and `get_acquisition_function` (#1932).
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* Enable use of `qLogExpectedImprovement` and `qLogNoisyExpectedImprovement` with Ax (#1941).
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#### Bug Fixes
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* Enable pathwise sampling modules to be converted to GPU (#1821).
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* Allow `Standardize` modules to be loaded once trained (#1874).
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* Fix memory leak in Inducing Point Allocators (#1890).
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* Correct einsum computation in `LCEAKernel` (#1918).
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* Properly whiten bounds in MVNXPB (#1933).
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* Make `FixedFeatureAcquisitionFunction` convert floats to double-precision tensors rather than single-precision (#1944).
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* Fix memory leak in `FullyBayesianPosterior` (#1951).
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* Make `AnalyticExpectedUtilityOfBestOption` input constructor work correctionly with multi-task GPs (#1955).
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## [0.8.5] - May 8, 2023
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#### New Features

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