The release log for BoTorch.

• Enable creating a new Ax release

• Support post_processing_func in optimize_with_nsgaii for post-processing optimization results, e.g., to round discrete dimensions to valid values (#3215).

• Require GPyTorch>=1.15.2 and linear_operator>=0.6.1 (#3182).

• Fix BAxUS bugs (#3204).
• Fix q-dim bug in ScalarizedPosteriorMean (#3191).
• Project candidate_set in qMultiFidelityMaxValueEntropy.__init__ before passing to super().__init__, allowing candidate_set to be either n x d (without fidelity dims) or n x (d + s) (with fidelity dims) (#3205).
• Fix bug in optimize_acqf_mixed_alternating that may produce candidates with invalid values when using parameter constraints on discrete parameters (#3212).

• Make d and target_fidelities required arguments in project_to_target_fidelity to eliminate silent bugs (#3203).
• Add test harness infrastructure for acquisition function testing (#3190, #3192).
• Fix typos and docstring issues across botorch (#3211).
• Fix docstring issues in analytic acquisition functions (#3208).

• Require Python>=3.11 and PyTorch>=2.2 (#3152).
• Require GPyTorch>=1.15.1 (#3181).

• Efficient leave-one-out cross-validation for Gaussian processes (#3098) and ensemble models (#3103).
• TrajectoryPlanningProblem test problem (#3182).

• Feasibility-driven Trust Region Bayesian Optimization tutorial (#3048).
• HIPE tutorial notebook (#3102).
• Optimization help example notebook (#3148).

• Fix the shape of NoisyExpectedHypervolumeMixin._initial_hvs (#3090).
• Fix KroneckerMultiTaskGP.posterior transforming inputs twice (#3132).
• Fix minor bugs in safe_math.py (#3172).

• Make SAAS prior sampleable (#3105).
• Add retries to optimize_with_nsgaii (#3116).
• Allow PosteriorTransform API to optionally accept features X (#3117).
• Clean up get_constants_like usage in botorch/utils/probability (#3131).
• Improve gradient stability in BVN (#3143).
• Enable evaluation of MultiTaskGP with unobserved tasks (#3145).
• Simplify task value remapping API (#3163).
• Update optimize_acqf_homotopy to support mixed optimization (#3165).
• Support custom data covariance modules in SingleTaskMultifidelityGP (#3167).
• Add task-level means to SaasFullyBayesianMultiTaskGP (#3175).
• Use HadamardGaussianLikelihood in HeterogeneousMTGP for inferred noise (#3176).
• Add multi-task mixin support to PyroModel hierarchy (#3177).

• Remove deprecated APIs for v0.17 (#3134).
  • get_fitted_map_saas_ensemble
  • qMultiObjectiveMaxValueEntropy
  • FullyBayesianPosterior
  • task_feature parameter from SingleTaskGP.construct_inputs
  • fixed_features argument from optimize_acqf_homotopy

• HIPE acquisition function (#3083, #3108).
• Clamp to 0 to avoid sqrt of negative numbers in mvn_hellinger_distance (#3109).
• Add support for styles to PFN models (#3099, #3114).
• Batched NEI with PFNs, enable more styles in botorch PFNs (#3113).

• New default parameterization for MultiTaskGP (#3049). See discussion: meta-pytorch#3065

  • Add PositiveIndexKernel (#3047).

• Add HeterogeneousMTGP for transfer learning between different search spaces (#3073).

• Add missing int cast in MCSampler (#3062).
• Fix shape error in qNegIntegratedPosteriorVariance (#3068).
• Add taus to the model state dict for EnsembleMapSaasSingleTaskGP for consistency when loading state dict (#3069).
• Fix confusing warning when using input transforms with deterministic models (#3078).

• Structure options arguments in gen_candidates_torch (#3019).
• Set default cache_root based on whether the model supports it (#3075).
• Use nanmean and nanstd in Standardize outcome transform (#3072).

• Require GPyTorch>=1.14.2 (#3055).

• Add EnsembleMapSaasSingleTaskGP (#3035, #3038, #3040).
• Allow different inferred noise levels for each task in MultitaskGP (#2997).
• Allow LatentKroneckerGP model to support different T values at train and test time (#3032, #3037).
• Allow qHypervolumeKnowledgeGradient to return log values for better numerical stability (#2974, #2976, #2979).
• Add NumericToCategoricalEncoding input transform (#2907).
• Added a MatheronPathModel - a DeterministicModel returning a Matheron path sample (#2984).
• Project points generated in acquisition function optization to the feasible space (#3010).
• Add support for non-uniform model weights in EnsembleModel and EnsemblePosterior (#2993).
• Allow optimizers to support negative indices for fixed features (#2970).
• Add worst known feasible value to constrained test problems (#3016).

• Fix optimize_acqf_mixed_alternating initialization with categorical features (#2986).
• Use IIDNormalSampler for PosteriorList by default to fix issue with correlated Sobol samples (#2977).
• Fix condition_on_observations to correctly apply input transforms and properly add data to train_inputs (#2989, #2990, #3034).
• Fix handling of input transforms for AdditiveMapSaasSingleTaskGP (#3042).
• Preserve train inputs and targets through transforms (#3044).
• Improve how qNEHVI handles pending points to avoid duplicate suggestions when initial pending points are passed (#2985).

• Add support for missing tasks in multi-task GP models (#2960).
• Add input constructor for LogConstrainedExpectedImprovement (#2973).
• Improve error handling and update documentation for inter-point constraints (#3003).
• Make AnalyticAcquisitionFunction._mean_and_sigma() return output dim consistently (#3028).
• Improve initialization with continuous relaxation in optimize_acqf_mixed_alternating (#3041).
• Implement ContextualDataset.__eq__() (#3005).
• Check shape of state dict when comparing input transforms (#3051).
• Add py.typed file to precent tools complainnig about type stubs (#2982).
• Improve best feasible objective computation; point user to use probability of feasibility (#3011).

• Deprecate get_fitted_map_saas_ensemble() in favor of EnsembleMapSaasSingleTaskGP (#3036).

• Add MCAcquisition support to PFNModel (#3031).
• Add copula-based multivariate posterior for PFNModel (#3045).
• Allow PFNModel to load checkpoints from trainings done with automl/PFNs (#3017).
• Add support for Kaiming/He initialization for the VBLL mean (#3053).

This is a compatibility release, coming only one week after 0.15.0.

• Enable optimizing a sequence of acquisition functions in optimize_acqf (#2931).

• NP Regression Model w/ LIG Acquisition (#2683).
• Fully Bayesian Matern GP with dimension scaling prior (#2855).
• Option for input warping in non-linear fully Bayesian GPs (#2858).
• Support for condition_on_observations in FullyBayesianMultiTaskGP (#2871).
• Improvements to optimize_acqf_mixed_alternating:
  • Support categoricals in alternating optimization (#2866).
  • Batch mixed optimization (#2895).
  • Non-equidistant discrete dimensions for optimize_acqf_mixed_alternating (#2923).
  • Update syntax for categoricals in optimize_acqf_mixed_alternating (#2942).
  • Equality constraints for optimize_acqf_mixed_alternating (#2944).
• Multi-output acquisition functions and related utilities:
  • Multi-Output Acquisition Functions (#2935).
  • Utility for greedily selecting an approximate hypervolume maximizing subset (#2936).
  • Update optimize with NSGA-II (#2937).
  • Add utility for running pymoo NSGA-II (#2868).
• Batched L-BFGS-B for more efficient acquisition function optimization (#2870, #2892).
• Pathwise Thompson sampling for ensemble models (#2877).
• ROBOT tutorial notebook (#2883).
• Add community notebooks to the botorch.org website (#2913).

• Fix model paths in prior fitted networks (#2843).
• Fix a bug where input transforms were not applied in fully Bayesian models in train mode (#2859).
• Fix local Y vs global Y_Train in generate_batch function in TURBO tutorial (#2862).
• Fix CUDA support for FullyBayesianMTGP (#2875).
• Fix edge case with NaNs in is_non_dominated (#2925).
• Normalize for correct fidelity in qLowerBoundMaxValueEntropy (#2930).
• Bug: Botorch_community VBLLModel posterior doesn't work with single value tensor (#2929).
• Fix variance shape bug in Riemann posterior (#2939).
• Fix input constructor for LogProbabilityOfFeasibility (#2945).
• Fix AugmentedRosenbrock problem and expand testing for optimizers (#2950).

• Improved documentation for optimize_acqf (#2865).
• Fully Bayesian Multi-Task GP cleanup (#2869).
• average_over_ensemble_models decorator for acquisition functions (#2873).
• Changes to I-BNN tutorial (#2889).
• Allow batched fixed features in gen_candidates_scipy and gen_candidates_torch (#2893)
• Refactor of MultiTask / FullyBayesianMultiTaskGP to use ProductKernel and IndexKernel (#2908).
• Various changes to PFNs to improve Ax compatibility (#2915, #2940).
• Eliminate expensive indexing in separate_mtmvn (#2920).
• Added reset method to StoppingCriterion (#2927).
• Simplify closure dispatch (#2947).
• Add BaseTestProblem.is_minimization_problem property (#2949).
• Simplify NdarrayOptimizationClosure (#2951).

• Prior Fitted Network (PFN) surrogate model integration (#2784).
• Variational Bayesian last-layer models as surrogate Models (#2754).
• Probabilities of feasibility for classifier-based constraints in acquisition functions (#2776).

• Helper for evaluating feasibility of candidate points (#2733).
  • Check for feasibility in gen_candidates_scipy and error out for infeasible candidates (#2737).
  • Return a feasible candidate if there is one and return_best_only=True (#2778).
• Allow for observation noise without provided evaluation_mask mask in ModelListGP (#2735).
• Implement incremental qLogNEI via incremental argument to qLogNoisyExpectedImprovement (#2760).
• Add utility for computing AIC/BIC/MLL from a model (#2785).
• New test functions:
  • Multi-fidelity test functions with discrete fidelities (#2796).
  • Keane bump function (#2802).
  • Mixed Ackley test function (#2830).
  • LABS test function (#2832).
• Add parameter types to test functions to support problems defined in mixed / discrete spaces (#2809).
  • Add input validation to test functions (#2829).
• Add [q]LogProbabilityOfFeasibility acquisition functions (#2815).

• Remove hard-coded dtype from best_f buffers (#2725).
• Fix dtype/nan issue in StratifiedStandardize (#2757).
• Properly handle observed noise in AdditiveMapSaasSingleTaskGP with outcome transforms (#2763).
• Do not count STOPPED (due to specified budget) as a model fitting failure (#2767).
• Ensure that initialize_q_batch always includes the maximum value when called in batch mode (#2773).
• Fix posterior with observation noise in batched MTGP models (#2782).
• Detach tensor in gen_candidates_scipy to avoid test failure due to new warning (#2797).
• Fix batch computation in Pivoted Cholesky (#2823).

• Add optimal values for synthetic contrained optimization problems (#2730).
  • Update max_hv and reference point for Penicillin problem (#2771).
  • Add optimal value to SpeedReducer problem (#2799).
• Update nonlinear_constraint_is_feasible to return a boolean tensor (#2731).
• Restructure sampling methods for info-theoretic acquisition functions (#2753).
• Prune baseline points in qLogNEI by default (#2762).
• Misc updates to MES-based acqusition functions (#2769).
• Pass option to reset submodules in train method for fully Bayesian models (#2783).
• Put outcome transforms into train mode in model constructors (#2817).
• LogEI: select cache_root based on model support (#2820).
• Remove Ax dependency from BoTorch tutorials and reference Ax tutorials instead (#2839).

• Remove deprecated gp_sampling module (#2768).
• Remove qMultiObjectiveMaxValueEntropy acquisition function (#2800).
• Remove model converters (#2801).

• BoTorch website has been upgraded to utilize Docusaurus v3, with the API reference being hosted by ReadTheDocs. The tutorials now expose an option to open with Colab, for easy access to a runtime with modifiable tutorials. The old versions of the website can be found at archive.botorch.org (#2653).
• RobustRelevancePursuitSingleTaskGP, a robust Gaussian process model that adaptively identifies outliers and leverages Bayesian model selection (paper) (#2608, #2690, #2707).
• LatentKroneckerGP, a scalable model for data on partially observed grids, like the joint modeling of hyper-parameters and partially completed learning curves in AutoML (paper) (#2647).
• Add MAP-SAAS model, which utilizes the sparse axis-aligned subspace priors (paper) with MAP model fitting (#2694).

• Require GPyTorch==1.14 and linear_operator==0.6 (#2710).
• Remove support for anaconda (official package) (#2617).
• Remove mpmath dependency pin (#2640).
• Updates to optimization routines to support SciPy>1.15:
  • Use threadpoolctl in minimize_with_timeout to prevent CPU oversubscription (#2712).
  • Update optimizer output parsing to make model fitting compatible with SciPy>1.15 (#2667).

• Add support for priors in OAK Kernel (#2535).
• Add BatchBroadcastedTransformList, which broadcasts a list of InputTransforms over batch shapes (#2558).
• InteractionFeatures input transform (#2560).
• Implement percentile_of_score, which takes inputs data and score, and returns the percentile of values in data that are below score (#2568).
• Add optimize_acqf_mixed_alternating, which supports optimization over mixed discrete & continuous spaces (#2573).
• Add support for PosteriorTransform to get_optimal_samples and optimize_posterior_samples (#2576).
• Support inequality constraints & X_avoid in optimize_acqf_discrete (#2593).
• Add ability to mix batch initial conditions and internal IC generation (#2610).
• Add qPosteriorStandardDeviation acquisition function (#2634).
• TopK downselection for initial batch generation. (#2636).
• Support optimization over mixed spaces in optimize_acqf_homotopy (#2639).
• Add InfeasibilityError exception class (#2652).
• Support InputTransforms in SparseOutlierLikelihood and get_posterior_over_support (#2659).
• StratifiedStandardize outcome transform (#2671).
• Add center argument to Normalize (#2680).
• Add input normalization step in Warp input transform (#2692).
• Support mixing fully Bayesian & SingleTaskGP models in ModelListGP (#2693).
• Add abstract fully Bayesian GP class and fully Bayesian linear GP model (#2696, #2697).
• Tutorial on BO constrained by probability of classification model (#2700).

• Fix error in decoupled_mobo tutorial due to torch/numpy issues (#2550).
• Raise error for MTGP in batch_cross_validation (#2554).
• Fix posterior method in BatchedMultiOutputGPyTorchModel for tracing JIT (#2592).
• Replace hard-coded double precision in test_functions with default dtype (#2597).
• Remove as_tensor argument of set_tensors_from_ndarray_1d (#2615).
• Skip fixed feature enumerations in optimize_acqf_mixed that can't satisfy the parameter constraints (#2614).
• Fix get_default_partitioning_alpha for >7 objectives (#2646).
• Fix random seed handling in sample_hypersphere (#2688).
• Fix bug in optimize_objective with fixed features (#2691).
• FullyBayesianSingleTaskGP.train should not return None (#2702).

• More efficient sampling from KroneckerMultiTaskGP (#2460).
• Update HigherOrderGP to use new priors & standardize outcome transform by default (#2555).
• Update initialize_q_batch methods to return both candidates and the corresponding acquisition values (#2571).
• Update optimization documentation with LogEI insights (#2587).
• Make all arguments in optimize_acqf_homotopy explicit (#2588).
• Introduce trial_indices argument to SupervisedDataset (#2595).
• Make optimizers raise an error when provided negative indices for fixed features (#2603).
• Make input transforms Modules by default (#2607).
• Reduce memory usage in ConstrainedMaxPosteriorSampling (#2622).
• Add clone method to datasets (#2625).
• Add support for continuous relaxation within optimize_acqf_mixed_alternating (#2635).
• Update indexing in qLogNEI._get_samples_and_objectives to support multiple input batches (#2649).
• Pass X to OutcomeTransforms (#2663).
• Use mini-batches when evaluating candidates within optimize_acqf_discrete_local_search (#2682).

• Remove HeteroskedasticSingleTaskGP (#2616).
• Remove FixedNoiseDataset (#2626).
• Remove support for legacy format non-linear constraints (#2627).
• Remove maximize option from information theoretic acquisition functions (#2590).

• Update most models to use dimension-scaled log-normal hyperparameter priors by default, which makes performance much more robust to dimensionality. See discussion #2451 for details. The only models that are not changed are those for fully Bayesian models and PairwiseGP; for models that utilize a composite kernel, such as multi-fidelity/task/context, this change only affects the base kernel (#2449, #2450, #2507).
• Use Standarize by default in all the models using the upgraded priors. In addition to reducing the amount of boilerplate needed to initialize a model, this change was motivated by the change to default priors, because the new priors will work less well when data is not standardized. Users who do not want to use transforms should explicitly pass in None (#2458, #2532).

• Unpin NumPy (#2459).
• Require PyTorch>=2.0.1, GPyTorch==1.13, and linear_operator==0.5.3 (#2511).

• Introduce PathwiseThompsonSampling acquisition function (#2443).
• Enable qBayesianActiveLearningByDisagreement to accept a posterior transform, and improve its implementation (#2457).
• Enable SaasPyroModel to sample via NUTS when training data is empty (#2465).
• Add multi-objective qBayesianActiveLearningByDisagreement (#2475).
• Add input constructor for qNegIntegratedPosteriorVariance (#2477).
• Introduce qLowerConfidenceBound (#2517).
• Add input constructor for qMultiFidelityHypervolumeKnowledgeGradient (#2524).
• Add posterior_transform to ApproximateGPyTorchModel.posterior (#2531).

• Fix batch_shape default in OrthogonalAdditiveKernel (#2473).
• Ensure all tensors are on CPU in HitAndRunPolytopeSampler (#2502).
• Fix duplicate logging in generation/gen.py (#2504).
• Raise exception if X_pending is set on the underlying AcquisitionFunction in prior-guided AcquisitionFunction (#2505).
• Make affine input transforms error with data of incorrect dimension, even in eval mode (#2510).
• Use fidelity-aware current_value in input constructor for qMultiFidelityKnowledgeGradient (#2519).
• Apply input transforms when computing MLL in model closures (#2527).
• Detach fval in torch_minimize to remove an opportunity for memory leaks (#2529).

• Clarify incompatibility of inter-point constraints with get_polytope_samples (#2469).
• Update tutorials to use the log variants of EI-family acquisition functions, don't make tutorials pass Standardize unnecessarily, and other simplifications and cleanup (#2462, #2463, #2490, #2495, #2496, #2498, #2499).
• Remove deprecated FixedNoiseGP (#2536).

• More informative warnings about failure to standardize or normalize data (#2489).
• Suppress irrelevant warnings in qHypervolumeKnowledgeGradient helpers (#2486).
• Cleaner botorch/acquisition/multi_objective directory structure (#2485).
• With AffineInputTransform, always require data to have at least two dimensions (#2518).
• Remove deprecated argument data_fidelity to SingleTaskMultiFidelityGP and deprecated model FixedNoiseMultiFidelityGP (#2532).
• Raise an OptimizationGradientError when optimization produces NaN gradients (#2537).
• Improve numerics by replacing torch.log(1 + x) with torch.log1p(x) and torch.exp(x) - 1 with torch.special.expm1 (#2539, #2540, #2541).

• Pin NumPy to <2.0 (#2382).
• Require GPyTorch 1.12 and LinearOperator 0.5.2 (#2408, #2441).

• Support evaluating posterior predictive in MultiTaskGP (#2375).
• Infinite width BNN kernel (#2366) and the corresponding tutorial (#2381).
• An improved elliptical slice sampling implementation (#2426).
• Add a helper for producing a DeterministicModel using a Matheron path (#2435).

• Stop allowing some arguments to be ignored in acqf input constructors (#2356).
• Reap deprecated **kwargs argument from optimize_acqf variants (#2390).
• Delete DeterministicPosterior and DeterministicSampler (#2391, #2409, #2410).
• Removed deprecated CachedCholeskyMCAcquisitionFunction (#2399).
• Deprecate model conversion code (#2431).
• Deprecate gp_sampling module in favor of pathwise sampling (#2432).

• Fix observation noise shape for batched models (#2377).
• Fix sample_all_priors to not sample one value for all lengthscales (#2404).
• Make (Log)NoisyExpectedImprovement create a correct fantasy model with non-default SingleTaskGP (#2414).

• Various documentation improvements (#2395, #2425, #2436, #2437, #2438).
• Clean up **kwargs arguments in qLogNEI (#2406).
• Add a NumericsWarning for Legacy EI implementations (#2429).

See 0.11.3 release. This release failed due to mismatching GPyTorch and LinearOperator versions.

• Implement qLogNParEGO (#2364).
• Support picking best of multiple fit attempts in fit_gpytorch_mll (#2373).

• Many functions that used to silently ignore arbitrary keyword arguments will now raise an exception when passed unsupported arguments (#2327, #2336).
• Remove UnstandardizeMCMultiOutputObjective and UnstandardizePosteriorTransform (#2362).

• Remove correlation between the step size and the step direction in sample_polytope (#2290).
• Fix pathwise sampler bug (#2337).
• Explicitly check timeout against None so that 0.0 isn't ignored (#2348).
• Fix boundary handling in sample_polytope (#2353).
• Avoid division by zero in normalize & unnormalize when lower & upper bounds are equal (#2363).
• Update sample_all_priors to support wider set of priors (#2371).

• Clarify is_non_dominated behavior with NaN (#2332).
• Add input constructor for qEUBO (#2335).
• Add LogEI as a baseline in the TuRBO tutorial (#2355).
• Update polytope sampling code and add thinning capability (#2358).
• Add initial objective values to initial state for sample efficiency (#2365).
• Clarify behavior on standard deviations with <1 degree of freedom (#2357).

• Reqire Python >= 3.10 (#2293).

• SCoreBO and Bayesian Active Learning acquisition functions (#2163).

• Fix non-None constraint noise levels in some constrained test problems (#2241).
• Fix inverse cost-weighted utility behaviour for non-positive acquisition values (#2297).

• Don't allow unused keyword arguments in Model.construct_inputs (#2186).
• Re-map task values in MTGP if they are not contiguous integers starting from zero (#2230).
• Unify ModelList and ModelListGP subset_output behavior (#2231).
• Ensure mean and interior_point of LinearEllipticalSliceSampler have correct shapes (#2245).
• Speed up task covariance of LCEMGP (#2260).
• Improvements to batch_cross_validation, support for model init kwargs (#2269).
• Support custom all_tasks for MTGPs (#2271).
• Error out if scipy optimizer does not support bounds / constraints (#2282).
• Support diagonal covariance root with fixed indices for LinearEllipticalSliceSampler (#2283).
• Make qNIPV a subclass of AcquisitionFunction rather than AnalyticAcquisitionFunction (#2286).
• Increase code-sharing of LCEMGP & define construct_inputs (#2291).

• Remove deprecated args from base MCSampler (#2228).
• Remove deprecated botorch/generation/gen/minimize (#2229).
• Remove fit_gpytorch_model (#2250).
• Remove requires_grad_ctx (#2252).
• Remove base_samples argument of GPyTorchPosterior.rsample (#2254).
• Remove deprecated mvn argument to GPyTorchPosterior (#2255).
• Remove deprecated Posterior.event_shape (#2320).
• Remove **kwargs & deprecated indices argument of Round transform (#2321).
• Remove Standardize.load_state_dict (#2322).
• Remove FixedNoiseMultiTaskGP (#2323).

• Introduce updated guidelines and a new directory for community contributions (#2167).
• Add qEUBO preferential acquisition function (#2192).
• Add Multi Information Source Augmented GP (#2152).

• Fix condition_on_observations in fully Bayesian models (#2151).
• Fix for bug that occurs when splitting single-element bins, use default BoTorch kernel for BAxUS. (#2165).
• Fix a bug when non-linear constraints are used with q > 1 (#2168).
• Remove unsupported X_pending from qMultiFidelityLowerBoundMaxValueEntropy constructor (#2193).
• Don't allow data_fidelities=[] in SingleTaskMultiFidelityGP (#2195).
• Fix EHVI, qEHVI, and qLogEHVI input constructors (#2196).
• Fix input constructor for qMultiFidelityMaxValueEntropy (#2198).
• Add ability to not deduplicate points in _is_non_dominated_loop (#2203).

• Minor improvements to MVaR risk measure (#2150).
• Add support for multitask models to ModelListGP (#2154).
• Support unspecified noise in ContextualDataset (#2155).
• Update HVKG sampler to reflect the number of model outputs (#2160).
• Release restriction in OneHotToNumeric that the categoricals are the trailing dimensions (#2166).
• Standardize broadcasting logic of q(Log)EI's best_f and compute_best_feasible_objective (#2171).
• Use regular inheritance instead of dispatcher to special-case PairwiseGP logic (#2176).
• Support PBO in EUBO's input constructor (#2178).
• Add posterior_transform to qMaxValueEntropySearch's input constructor (#2181).
• Do not normalize or standardize dimension if all values are equal (#2185).
• Reap deprecated support for objective with 1 arg in GenericMCObjective (#2199).
• Consistent signature for get_objective_weights_transform (#2200).
• Update context order handling in ContextualDataset (#2205).
• Update contextual models for use in MBM (#2206).
• Remove (Identity)AnalyticMultiOutputObjective (#2208).
• Reap deprecated support for soft_eval_constraint (#2223). Please use botorch.utils.sigmoid instead.

• Pin mpmath <= 1.3.0 to avoid CI breakages due to removed modules in the latest alpha release (#2222).

Hypervolume Knowledge Gradient (HVKG):

• Add qHypervolumeKnowledgeGradient, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950, #1982, #2101).
• Add tutorial on decoupled Multi-Objective Bayesian Optimization (MOBO) with HVKG (#2094).

Other new features:

• Add MultiOutputFixedCostModel, which is useful for decoupled scenarios where the objectives have different costs (#2093).
• Enable q > 1 in acquisition function optimization when nonlinear constraints are present (#1793).
• Support different noise levels for different outputs in test functions (#2136).

• Fix fantasization with a FixedNoiseGaussianLikelihood when noise is known and X is empty (#2090).
• Make LearnedObjective compatible with constraints in acquisition functions regardless of sample_shape (#2111).
• Make input constructors for qExpectedImprovement, qLogExpectedImprovement, and qProbabilityOfImprovement compatible with LearnedObjective regardless of sample_shape (#2115).
• Fix handling of constraints in qSimpleRegret (#2141).

• Increase default sample size for LearnedObjective (#2095).
• Allow passing in X with or without fidelity dimensions in project_to_target_fidelity (#2102).
• Use full-rank task covariance matrix by default in SAAS MTGP (#2104).
• Rename FullyBayesianPosterior to GaussianMixturePosterior; add _is_ensemble and _is_fully_bayesian attributes to Model (#2108).
• Various improvements to tutorials including speedups, improved explanations, and compatibility with newer versions of libraries.

• Re-establish compatibility with PyTorch 1.13.1 (#2083).

• Additional "Log" acquisition functions for multi-objective optimization with better numerical behavior, which often leads to significantly improved BO performance over their non-"Log" counterparts:
  • qLogEHVI (#2036).
  • qLogNEHVI (#2045, #2046, #2048, #2051).
  • Support fully Bayesian models with LogEI-type acquisition functions (#2058).
• FixedNoiseGP and FixedNoiseMultiFidelityGP have been deprecated, their functionalities merged into SingleTaskGP and SingleTaskMultiFidelityGP, respectively (#2052, #2053).
• Removed deprecated legacy model fitting functions: numpy_converter, fit_gpytorch_scipy, fit_gpytorch_torch, _get_extra_mll_args (#1995, #2050).

• Support multiple data fidelity dimensions in SingleTaskMultiFidelityGP and (deprecated) FixedNoiseMultiFidelityGP models (#1956).
• Add logsumexp and fatmax to handle infinities and control asymptotic behavior in "Log" acquisition functions (#1999).
• Add outcome and feature names to datasets, implement MultiTaskDataset (#2015, #2019).
• Add constrained Hartmann and constrained Gramacy synthetic test problems (#2022, #2026, #2027).
• Support observed noise in MixedSingleTaskGP (#2054).
• Add PosteriorStandardDeviation acquisition function (#2060).

• Fix input constructors for qMaxValueEntropy and qMultiFidelityKnowledgeGradient (#1989).
• Fix precision issue that arises from inconsistent data types in LearnedObjective (#2006).
• Fix fantasization with FixedNoiseGP and outcome transforms and use FantasizeMixin (#2011).
• Fix LearnedObjective base sample shape (#2021).
• Apply constraints in prune_inferior_points (#2069).
• Support non-batch evaluation of PenalizedMCObjective (#2073).
• Fix Dataset equality checks (#2077).

• Don't allow unused **kwargs in input_constructors except for a defined set of exceptions (#1872, #1985).
• Merge inferred and fixed noise LCE-M models (#1993).
• Fix import structure in botorch.acquisition.utils (#1986).
• Remove deprecated functionality: weights argument of RiskMeasureMCObjective and squeeze_last_dim (#1994).
• Make X, Y, Yvar into properties in datasets (#2004).
• Make synthetic constrained test functions subclass from SyntheticTestFunction (#2029).
• Add construct_inputs to contextual GP models LCEAGP and SACGP (#2057).

• Hot fix (#1973) for a few issues:
  • A naming mismatch between Ax's modular BotorchModel and the BoTorch's acquisition input constructors, leading to outcome constraints in Ax not being used with single-objective acquisition functions in Ax's modular BotorchModel. The naming has been updated in Ax and consistent naming is now used in input constructors for single and multi-objective acquisition functions in BoTorch.
  • A naming mismatch in the acquisition input constructor constraints in qNoisyLogExpectedImprovement, which kept constraints from being used.
  • A bug in compute_best_feasible_objective that could lead to -inf incumbent values.
• Fix setting seed in get_polytope_samples (#1968)

• Merge SupervisedDataset and FixedNoiseDataset (#1945).
• Constrained tutorial updates (#1967, #1970).
• Resolve issues with missing pytorch binaries with py3.11 on Mac (#1966).

• Require linear_operator == 0.5.1 (#1963).

• Require Python >= 3.9.0 (#1924).
• Require PyTorch >= 1.13.1 (#1960).
• Require linear_operator == 0.5.0 (#1961).
• Require GPyTorch == 1.11 (#1961).

• Introduce OrthogonalAdditiveKernel (#1869).
• Speed up LCE-A kernel by over an order of magnitude (#1910).
• Introduce optimize_acqf_homotopy, for optimizing acquisition functions with homotopy (#1915).
• Introduce PriorGuidedAcquisitionFunction (PiBO) (#1920).
• Introduce qLogExpectedImprovement, which provides more accurate numerics than qExpectedImprovement and can lead to significant optimization improvements (#1936).
• Similarly, introduce qLogNoisyExpectedImprovement, which is analogous to qNoisyExpectedImprovement (#1937).

• Add constrained synthetic test functions PressureVesselDesign, WeldedBeam, SpeedReducer, and TensionCompressionString (#1832).
• Support decoupled fantasization (#1853) and decoupled evaluations in cost-aware utilities (#1949).
• Add PairwiseBayesianActiveLearningByDisagreement, an active learning acquisition function for PBO and BOPE (#1855).
• Support custom mean and likelihood in MultiTaskGP (#1909).
• Enable candidate generation (via optimize_acqf) with both non_linear_constraints and fixed_features (#1912).
• Introduce L0PenaltyApproxObjective to support L0 regularization (#1916).
• Enable batching in PriorGuidedAcquisitionFunction (#1925).

• Deprecate FixedNoiseMultiTaskGP; allow train_Yvar optionally in MultiTaskGP (#1818).
• Implement load_state_dict for SAAS multi-task GP (#1825).
• Improvements to LinearEllipticalSliceSampler (#1859, #1878, #1879, #1883).
• Allow passing in task features as part of X in MTGP.posterior (#1868).
• Improve numerical stability of log densities in pairwise GPs (#1919).
• Python 3.11 compliance (#1927).
• Enable using constraints with SampleReducingMCAcquisitionFunctions when using input_constructors and get_acquisition_function (#1932).
• Enable use of qLogExpectedImprovement and qLogNoisyExpectedImprovement with Ax (#1941).

• Enable pathwise sampling modules to be converted to GPU (#1821).
• Allow Standardize modules to be loaded once trained (#1874).
• Fix memory leak in Inducing Point Allocators (#1890).
• Correct einsum computation in LCEAKernel (#1918).
• Properly whiten bounds in MVNXPB (#1933).
• Make FixedFeatureAcquisitionFunction convert floats to double-precision tensors rather than single-precision (#1944).
• Fix memory leak in FullyBayesianPosterior (#1951).
• Make AnalyticExpectedUtilityOfBestOption input constructor work correctionly with multi-task GPs (#1955).

• Support inferred noise in SaasFullyBayesianMultiTaskGP (#1809).

• More informative error message when Standardize has wrong batch shape (#1807).
• Make GIBBON robust to numerical instability (#1814).
• Add sample_multiplier in EUBO's acqf_input_constructor (#1816).

• Only do checks for _optimize_acqf_sequential_q when it will be used (#1808).
• Fix an issue where PairwiseGP comparisons might be implicitly modified (#1811).

• Require GPyTorch == 1.10 and linear_operator == 0.4.0 (#1803).

• Polytope sampling for linear constraints along the q-dimension (#1757).
• Single-objective joint entropy search with additional conditioning, various improvements to entropy-based acquisition functions (#1738).

• Various updates to improve numerical stability of PairwiseGP (#1754, #1755).
• Change batch range for FullyBayesianPosterior (1176a38352b69d01def0a466233e6633c17d6862, #1773).
• Make gen_batch_initial_conditions more flexible (#1779).
• Deprecate objective in favor of posterior_transform for MultiObjectiveAnalyticAcquisitionFunction (#1781).
• Use prune_baseline=True as default for qNoisyExpectedImprovement (#1796).
• Add batch_shape property to SingleTaskVariationalGP (#1799).
• Change minimum inferred noise level for SaasFullyBayesianSingleTaskGP (#1800).

• Add output_task to MultiTaskGP.construct_inputs (#1753).
• Fix custom bounds handling in test problems (#1760).
• Remove incorrect BotorchTensorDimensionWarning (#1790).
• Fix handling of non-Container-typed positional arguments in SupervisedDatasetMeta (#1663).

• Add BAxUS tutorial (#1559).

• Various improvements to tutorials (#1703, #1706, #1707, #1708, #1710, #1711, #1718, #1719, #1739, #1740, #1742).
• Allow tensor input for integer_indices in Round transform (#1709).
• Expose cache_root in qNEHVI input constructor (#1730).
• Add get_init_args helper to Normalize & Round transforms (#1731).
• Allowing custom dimensionality and improved gradient stability in ModifiedFixedSingleSampleModel (#1732).

• Improve batched model handling in _verify_output_shape (#1715).
• Fix qNEI with Derivative Enabled BO (#1716).
• Fix get_infeasible_cost for objectives that require X (#1721).

• Require PyTorch >= 1.12 (#1699).

• Introduce pathwise sampling API for efficiently sampling functions from (approximate) GP priors and posteriors (#1463).
• Add OneHotToNumeric input transform (#1517).
• Add get_rounding_input_transform utility for constructing rounding input transforms (#1531).
• Introduce EnsemblePosterior (#1636).
• Inducing Point Allocators for Sparse GPs (#1652).
• Pass gen_candidates callable in optimize_acqf (#1655).
• Adding logmeanexp and logdiffexp numerical utilities (#1657).

• Warn if inoperable keyword arguments are passed to optimizers (#1421).
• Add BotorchTestCase.assertAllClose (#1618).
• Add sample_shape property to ListSampler (#1624).
• Do not filter out BoTorchWarnings by default (#1630).
• Introduce a DeterministicSampler (#1641).
• Warn when optimizer kwargs are being ignored in BoTorch optim utils _filter_kwargs (#1645).
• Don't use functools.lru_cache on methods (#1650).
• More informative error when someone adds a module without updating the corresponding rst file (#1653).
• Make indices a buffer in AffineInputTransform (#1656).
• Clean up optimize_acqf and _make_linear_constraints (#1660, #1676).
• Support NaN max_reference_point in infer_reference_point (#1671).
• Use _fast_solves in HOGP.posterior (#1682).
• Approximate qPI using MVNXPB (#1684).
• Improve filtering for cache_root in CachedCholeskyMCAcquisitionFunction (#1688).
• Add option to disable retrying on optimization warning (#1696).

• Fix normalization in Chebyshev scalarization (#1616).
• Fix TransformedPosterior missing batch shape error in _update_base_samples (#1625).
• Detach coefficient and offset in AffineTransform in eval mode (#1642).
• Fix pickle error in TorchPosterior (#1644).
• Fix shape error in optimize_acqf_cyclic (#1648).
• Fixed bug where optimize_acqf didn't work with different batch sizes (#1668).
• Fix EUBO optimization error when two Xs are identical (#1670).
• Bug fix: _filter_kwargs was erroring when provided a function without a __name__ attribute (#1678).

• This release includes changes for compatibility with the newest versions of linear_operator and gpytorch.
• Several acquisition functions now have "Log" counterparts, which provide better numerical behavior for improvement-based acquisition functions in areas where the probability of improvement is low. For example, LogExpectedImprovement (#1565) should behave better than ExpectedImprovement. These new acquisition functions are
  • LogExpectedImprovement (#1565).
  • LogNoisyExpectedImprovement (#1577).
  • LogProbabilityOfImprovement (#1594).
  • LogConstrainedExpectedImprovement (#1594).
• Bug fix: Stop ModelListGP.posterior from quietly ignoring Log, Power, and Bilog outcome transforms (#1563).
• Turn off fast_computations setting in linear_operator by default (#1547).

• Require linear_operator == 0.3.0 (#1538).
• Require pyro-ppl >= 1.8.4 (#1606).
• Require gpytorch == 1.9.1 (#1612).

• Add eta to get_acquisition_function (#1541).
• Support 0d-features in FixedFeatureAcquisitionFunction (#1546).
• Add timeout ability to optimization functions (#1562, #1598).
• Add MultiModelAcquisitionFunction, an abstract base class for acquisition functions that require multiple types of models (#1584).
• Add cache_root option for qNEI in get_acquisition_function (#1608).

• Docstring corrections (#1551, #1557, #1573).
• Removal of _fit_multioutput_independent and allclose_mll (#1570).
• Better numerical behavior for fully Bayesian models (#1576).
• More verbose Scipy minimize failure messages (#1579).
• Lower-bound noise inSaasPyroModel to avoid Cholesky errors (#1586).

• Error rather than failing silently for NaN values in box decomposition (#1554).
• Make get_bounds_as_ndarray device-safe (#1567).

This release includes some backwards incompatible changes.

• Refactor Posterior and MCSampler modules to better support non-Gaussian distributions in BoTorch (#1486).
  • Introduced a TorchPosterior object that wraps a PyTorch Distribution object and makes it compatible with the rest of Posterior API.
  • PosteriorList no longer accepts Gaussian base samples. It should be used with a ListSampler that includes the appropriate sampler for each posterior.
  • The MC acquisition functions no longer construct a Sobol sampler by default. Instead, they rely on a get_sampler helper, which dispatches an appropriate sampler based on the posterior provided.
  • The resample and collapse_batch_dims arguments to MCSamplers have been removed. The ForkedRNGSampler and StochasticSampler can be used to get the same functionality.
  • Refer to the PR for additional changes. We will update the website documentation to reflect these changes in a future release.
• #1191 refactors much of botorch.optim to operate based on closures that abstract away how losses (and gradients) are computed. By default, these closures are created using multiply-dispatched factory functions (such as get_loss_closure), which may be customized by registering methods with an associated dispatcher (e.g. GetLossClosure). Future releases will contain tutorials that explore these features in greater detail.

• Add mixed optimization for list optimization (#1342).
• Add entropy search acquisition functions (#1458).
• Add utilities for straight-through gradient estimators for discretization functions (#1515).
• Add support for categoricals in Round input transform and use STEs (#1516).
• Add closure-based optimizers (#1191).

• Do not count hitting maxiter as optimization failure & update default maxiter (#1478).
• BoxDecomposition cleanup (#1490).
• Deprecate torch.triangular_solve in favor of torch.linalg.solve_triangular (#1494).
• Various docstring improvements (#1496, #1499, #1504).
• Remove __getitem__ method from LinearTruncatedFidelityKernel (#1501).
• Handle Cholesky errors when fitting a fully Bayesian model (#1507).
• Make eta configurable in apply_constraints (#1526).
• Support SAAS ensemble models in RFFs (#1530).
• Deprecate botorch.optim.numpy_converter (#1191).
• Deprecate fit_gpytorch_scipy and fit_gpytorch_torch (#1191).

• Enforce use of float64 in NdarrayOptimizationClosure (#1508).
• Replace deprecated np.bool with equivalent bool (#1524).
• Fix RFF bug when using FixedNoiseGP models (#1528).

• #1454 fixes a critical bug that affected multi-output BatchedMultiOutputGPyTorchModels that were using a Normalize or InputStandardize input transform and trained using fit_gpytorch_model/mll with sequential=True (which was the default until 0.7.3). The input transform buffers would be reset after model training, leading to the model being trained on normalized input data but evaluated on raw inputs. This bug had been affecting model fits since the 0.6.5 release.
• #1479 changes the inheritance structure of Models in a backwards-incompatible way. If your code relies on isinstance checks with BoTorch Models, especially SingleTaskGP, you should revisit these checks to make sure they still work as expected.

• Require linear_operator == 0.2.0 (#1491).

• Introduce bvn, MVNXPB, TruncatedMultivariateNormal, and UnifiedSkewNormal classes / methods (#1394, #1408).
• Introduce AffineInputTransform (#1461).
• Introduce a subset_transform decorator to consolidate subsetting of inputs in input transforms (#1468).

• Add a warning when using float dtype (#1193).
• Let Pyre know that AcquisitionFunction.model is a Model (#1216).
• Remove custom BlockDiagLazyTensor logic when using Standardize (#1414).
• Expose _aug_batch_shape in SaasFullyBayesianSingleTaskGP (#1448).
• Adjust PairwiseGP ScaleKernel prior (#1460).
• Pull out fantasize method into a FantasizeMixin class, so it isn't so widely inherited (#1462, #1479).
• Don't use Pyro JIT by default , since it was causing a memory leak (#1474).
• Use get_default_partitioning_alpha for NEHVI input constructor (#1481).

• Fix batch_shape property of ModelListGPyTorchModel (#1441).
• Tutorial fixes (#1446, #1475).
• Bug-fix for Proximal acquisition function wrapper for negative base acquisition functions (#1447).
• Handle RuntimeError due to constraint violation while sampling from priors (#1451).
• Fix bug in model list with output indices (#1453).
• Fix input transform bug when sequentially training a BatchedMultiOutputGPyTorchModel (#1454).
• Fix a bug in _fit_multioutput_independent that failed mll comparison (#1455).
• Fix box decomposition behavior with empty or None Y (#1489).

• A full refactor of model fitting methods (#1134).
  • This introduces a new fit_gpytorch_mll method that multiple-dispatches on the model type. Users may register custom fitting routines for different combinations of MLLs, Likelihoods, and Models.
  • Unlike previous fitting helpers, fit_gpytorch_mll does not pass kwargs to optimizer and instead introduces an optional optimizer_kwargs argument.
  • When a model fitting attempt fails, botorch.fit methods restore modules to their original states.
  • fit_gpytorch_mll throws a ModelFittingError when all model fitting attempts fail.
  • Upon returning from fit_gpytorch_mll, mll.training will be True if fitting failed and False otherwise.
• Allow custom bounds to be passed in to SyntheticTestFunction (#1415).

• Deprecate weights argument of risk measures in favor of a preprocessing_function (#1400),
• Deprecate fit_gyptorch_model; to be superseded by fit_gpytorch_mll.

• Support risk measures in MOO input constructors (#1401).

• Fix fully Bayesian state dict loading when there are more than 10 models (#1405).
• Fix batch_shape property of SaasFullyBayesianSingleTaskGP (#1413).
• Fix model_list_to_batched ignoring the covar_module of the input models (#1419).

• Pin GPyTorch >= 1.9.0 (#1397).
• Pin linear_operator == 0.1.1 (#1397).

• Implement SaasFullyBayesianMultiTaskGP and related utilities (#1181, #1203).

• Support loading a state dict for SaasFullyBayesianSingleTaskGP (#1120).
• Update load_state_dict for ModelList to support fully Bayesian models (#1395).
• Add is_one_to_many attribute to input transforms (#1396).

• Fix PairwiseGP on GPU (#1388).

• Require python >= 3.8 (via #1347).
• Support for python 3.10 (via #1379).
• Require PyTorch >= 1.11 (via (#1363).
• Require GPyTorch >= 1.9.0 (#1347).
  • GPyTorch 1.9.0 is a major refactor that factors out the lazy tensor functionality into a new LinearOperator library, which required a number of adjustments to BoTorch (#1363, #1377).
• Require pyro >= 1.8.2 (#1379).

• Add ability to generate the features appended in the AppendFeatures input transform via a generic callable (#1354).
• Add new synthetic test functions for sensitivity analysis (#1355, #1361).

• Use time.monotonic() instead of time.time() to measure duration (#1353).
• Allow passing Y_samples directly in MARS.set_baseline_Y (#1364).

• Patch state_dict loading for PairwiseGP (#1359).
• Fix batch_shape handling in Normalize and InputStandardize transforms (#1360).

• Require GPyTorch >= 1.8.1 (#1347).

• Support batched models in RandomFourierFeatures (#1336).
• Add a skip_expand option to AppendFeatures (#1344).

• Allow qProbabilityOfImprovement to use batch-shaped best_f (#1324).
• Make optimize_acqf re-attempt failed optimization runs and handle optimization errors in optimize_acqf and gen_candidates_scipy better (#1325).
• Reduce memory overhead in MARS.set_baseline_Y (#1346).

• Fix bug where outcome_transform was ignored for ModelListGP.fantasize (#1338).
• Fix bug causing get_polytope_samples to sample incorrectly when variables live in multiple dimensions (#1341).

• Add more descriptive docstrings for models (#1327, #1328, #1329, #1330) and for other classes (#1313).
• Expanded on the model documentation at botorch.org/docs/models (#1337).

• Require PyTorch >=1.10 (#1293).
• Require GPyTorch >=1.7 (#1293).

• Add MOMF (Multi-Objective Multi-Fidelity) acquisition function (#1153).
• Support PairwiseLogitLikelihood and modularize PairwiseGP (#1193).
• Add in transformed weighting flag to Proximal Acquisition function (#1194).
• Add FeasibilityWeightedMCMultiOutputObjective (#1202).
• Add outcome_transform to FixedNoiseMultiTaskGP (#1255).
• Support Scalable Constrained Bayesian Optimization (#1257).
• Support SaasFullyBayesianSingleTaskGP in prune_inferior_points (#1260).
• Implement MARS as a risk measure (#1303).
• Add MARS tutorial (#1305).

• Add Bilog outcome transform (#1189).
• Make get_infeasible_cost return a cost value for each outcome (#1191).
• Modify risk measures to accept List[float] for weights (#1197).
• Support SaasFullyBayesianSingleTaskGP in prune_inferior_points_multi_objective (#1204).
• BotorchContainers and BotorchDatasets: Large refactor of the original TrainingData API to allow for more diverse types of datasets (#1205, #1221).
• Proximal biasing support for multi-output SingleTaskGP models (#1212).
• Improve error handling in optimize_acqf_discrete with a check that choices is non-empty (#1228).
• Handle X_pending properly in FixedFeatureAcquisition (#1233, #1234).
• PE and PLBO support in Ax (#1240, #1241).
• Remove model.train call from get_X_baseline for better caching (#1289).
• Support inf values in bounds argument of optimize_acqf (#1302).

• Update get_gp_samples to support input / outcome transforms (#1201).
• Fix cached Cholesky sampling in qNEHVI when using Standardize outcome transform (#1215).
• Make task_feature as required input in MultiTaskGP.construct_inputs (#1246).
• Fix CUDA tests (#1253).
• Fix FixedSingleSampleModel dtype/device conversion (#1254).
• Prevent inappropriate transforms by putting input transforms into train mode before converting models (#1283).
• Fix sample_points_around_best when using 20 dimensional inputs or prob_perturb (#1290).
• Skip bound validation in optimize_acqf if inequality constraints are specified (#1297).
• Properly handle RFFs when used with a ModelList with individual transforms (#1299).
• Update PosteriorList to support deterministic-only models and fix event_shape (#1300).

• Add a note about observation noise in the posterior in fit_model_with_torch_optimizer notebook (#1196).
• Fix custom botorch model in Ax tutorial to support new interface (#1213).
• Update MOO docs (#1242).
• Add SMOKE_TEST option to MOMF tutorial (#1243).
• Fix ModelListGP.condition_on_observations/fantasize bug (#1250).
• Replace space with underscore for proper doc generation (#1256).
• Update PBO tutorial to use EUBO (#1262).

• Implement ExpectationPosteriorTransform (#903).
• Add PairwiseMCPosteriorVariance, a cheap active learning acquisition function (#1125).
• Support computing quantiles in the fully Bayesian posterior, add FullyBayesianPosteriorList (#1161).
• Add expectation risk measures (#1173).
• Implement Multi-Fidelity GIBBON (Lower Bound MES) acquisition function (#1185).

• Add an error message for one shot acquisition functions in optimize_acqf_discrete (#939).
• Validate the shape of the bounds argument in optimize_acqf (#1142).
• Minor tweaks to SAASBO (#1143, #1183).
• Minor updates to tutorials (24f7fda7b40d4aabf502c1a67816ac1951af8c23, #1144, #1148, #1159, #1172, #1180).
• Make it easier to specify a custom PyroModel (#1149).
• Allow passing in a mean_module to SingleTaskGP/FixedNoiseGP (#1160).
• Add a note about acquisitions using gradients to base class (#1168).
• Remove deprecated box_decomposition module (#1175).

• Bug-fixes for ProximalAcquisitionFunction (#1122).
• Fix missing warnings on failed optimization in fit_gpytorch_scipy (#1170).
• Ignore data related buffers in PairwiseGP.load_state_dict (#1171).
• Make fit_gpytorch_model properly honor the debug flag (#1178).
• Fix missing posterior_transform in gen_one_shot_kg_initial_conditions (#1187).

• Implement SAASBO - SaasFullyBayesianSingleTaskGP model for sample-efficient high-dimensional Bayesian optimization (#1123).
• Add SAASBO tutorial (#1127).
• Add LearnedObjective (#1131), AnalyticExpectedUtilityOfBestOption acquisition function (#1135), and a few auxiliary classes to support Bayesian optimization with preference exploration (BOPE).
• Add BOPE tutorial (#1138).

• Use qKG.evaluate in optimize_acqf_mixed (#1133).
• Add construct_inputs to SAASBO (#1136).

• Fix "Constraint Active Search" tutorial (#1124).
• Update "Discrete Multi-Fidelity BO" tutorial (#1134).

• Use BOTORCH_MODULAR in tutorials with Ax (#1105).
• Add optimize_acqf_discrete_local_search for discrete search spaces (#1111).

• Fix missing posterior_transform in qNEI and get_acquisition_function (#1113).

• Add Standardize input transform (#1053).
• Low-rank Cholesky updates for NEI (#1056).
• Add support for non-linear input constraints (#1067).
• New MOO problems: MW7 (#1077), disc brake (#1078), penicillin (#1079), RobustToy (#1082), GMM (#1083).

• Support multi-output models in MES using PosteriorTransform (#904).
• Add Dispatcher (#1009).
• Modify qNEHVI to support deterministic models (#1026).
• Store tensor attributes of input transforms as buffers (#1035).
• Modify NEHVI to support MTGPs (#1037).
• Make Normalize input transform input column-specific (#1047).
• Improve find_interior_point (#1049).
• Remove deprecated botorch.distributions module (#1061).
• Avoid costly application of posterior transform in Kronecker & HOGP models (#1076).
• Support heteroscedastic perturbations in InputPerturbations (#1088).

• Make risk measures more memory efficient (#1034).

• Properly handle empty fixed_features in optimization (#1029).
• Fix missing weights in VaR risk measure (#1038).
• Fix find_interior_point for negative variables & allow unbounded problems (#1045).
• Filter out indefinite bounds in constraint utilities (#1048).
• Make non-interleaved base samples use intuitive shape (#1057).
• Pad small diagonalization with zeros for KroneckerMultitaskGP (#1071).
• Disable learning of bounds in preprocess_transform (#1089).
• Fix gen_candidates_torch (4079164489613d436d19c7b2df97677d97dfa8dc).
• Catch runtime errors with ill-conditioned covar (#1095).
• Fix compare_mc_analytic_acquisition tutorial (#1099).

• Require PyTorch >=1.9 (#1011).
• Require GPyTorch >=1.6 (#1011).

• New ApproximateGPyTorchModel wrapper for various (variational) approximate GP models (#1012).
• New SingleTaskVariationalGP stochastic variational Gaussian Process model (#1012).
• Support for Multi-Output Risk Measures (#906, #965).
• Introduce ModelList and PosteriorList (#829).
• New Constraint Active Search tutorial (#1010).
• Add additional multi-objective optimization test problems (#958).

• Add covar_module as an optional input of MultiTaskGP models (#941).
• Add min_range argument to Normalize transform to prevent division by zero (#931).
• Add initialization heuristic for acquisition function optimization that samples around best points (#987).
• Update initialization heuristic to perturb a subset of the dimensions of the best points if the dimension is > 20 (#988).
• Modify apply_constraints utility to work with multi-output objectives (#994).
• Short-cut t_batch_mode_transform decorator on non-tensor inputs (#991).

• Use lazy covariance matrix in BatchedMultiOutputGPyTorchModel.posterior (#976).
• Fast low-rank Cholesky updates for qNoisyExpectedHypervolumeImprovement (#747, #995, #996).

• Update error handling to new PyTorch linear algebra messages (#940).
• Avoid test failures on Ampere devices (#944).
• Fixes to the Griewank test function (#972).
• Handle empty base_sample_shape in Posterior.rsample (#986).
• Handle NotPSDError and hitting maxiter in fit_gpytorch_model (#1007).
• Use TransformedPosterior for subclasses of GPyTorchPosterior (#983).
• Propagate best_f argument to qProbabilityOfImprovement in input constructors (f5a5f8b6dc20413e67c6234e31783ac340797a8d).

• Require GPyTorch >=1.5.1 (#928).

• Add HigherOrderGP composite Bayesian Optimization tutorial notebook (#864).
• Add Multi-Task Bayesian Optimziation tutorial (#867).
• New multi-objective test problems from (#876).
• Add PenalizedMCObjective and L1PenaltyObjective (#913).
• Add a ProximalAcquisitionFunction for regularizing new candidates towards previously generated ones (#919, #924).
• Add a Power outcome transform (#925).

• Batch mode fix for HigherOrderGP initialization (#856).
• Improve CategoricalKernel precision (#857).
• Fix an issue with qMultiFidelityKnowledgeGradient.evaluate (#858).
• Fix an issue with transforms with HigherOrderGP. (#889)
• Fix initial candidate generation when parameter constraints are on different device (#897).
• Fix bad in-place op in _generate_unfixed_lin_constraints (#901).
• Fix an input transform bug in fantasize call (#902).
• Fix outcome transform bug in batched_to_model_list (#917).

• Make variance optional for TransformedPosterior.mean (#855).
• Support transforms in DeterministicModel (#869).
• Support batch_shape in RandomFourierFeatures (#877).
• Add a maximize flag to PosteriorMean (#881).
• Ignore categorical dimensions when validating training inputs in MixedSingleTaskGP (#882).
• Refactor HigherOrderGPPosterior for memory efficiency (#883).
• Support negative weights for minimization objectives in get_chebyshev_scalarization (#884).
• Move train_inputs transforms to model.train/eval calls (#894).

• Require PyTorch >=1.8.1 (#832).
• Require GPyTorch >=1.5 (#848).
• Changes to how input transforms are applied: transform_inputs is applied in model.forward if the model is in train mode, otherwise it is applied in the posterior call (#819, #835).

• Improved multi-objective optimization capabilities:
  • qNoisyExpectedHypervolumeImprovement acquisition function that improves on qExpectedHypervolumeImprovement in terms of tolerating observation noise and speeding up computation for large q-batches (#797, #822).
  • qMultiObjectiveMaxValueEntropy acqusition function (913aa0e510dde10568c2b4b911124cdd626f6905, #760).
  • Heuristic for reference point selection (#830).
  • FastNondominatedPartitioning for Hypervolume computations (#699).
  • DominatedPartitioning for partitioning the dominated space (#726).
  • BoxDecompositionList for handling box decompositions of varying sizes (#712).
  • Direct, batched dominated partitioning for the two-outcome case (#739).
  • get_default_partitioning_alpha utility providing heuristic for selecting approximation level for partitioning algorithms (#793).
  • New method for computing Pareto Frontiers with less memory overhead (#842, #846).
• New qLowerBoundMaxValueEntropy acquisition function (a.k.a. GIBBON), a lightweight variant of Multi-fidelity Max-Value Entropy Search using a Determinantal Point Process approximation (#724, #737, #749).
• Support for discrete and mixed input domains:
  • CategoricalKernel for categorical inputs (#771).
  • MixedSingleTaskGP for mixed search spaces (containing both categorical and ordinal parameters) (#772, #847).
  • optimize_acqf_discrete for optimizing acquisition functions over fully discrete domains (#777).
  • Extend optimize_acqf_mixed to allow batch optimization (#804).
• Support for robust / risk-aware optimization:
  • Risk measures for robust / risk-averse optimization (#821).
  • AppendFeatures transform (#820).
  • InputPerturbation input transform for for risk averse BO with implementation errors (#827).
  • Tutorial notebook for Bayesian Optimization of risk measures (#823).
  • Tutorial notebook for risk-averse Bayesian Optimization under input perturbations (#828).
• More scalable multi-task modeling and sampling:
  • KroneckerMultiTaskGP model for efficient multi-task modeling for block-design settings (all tasks observed at all inputs) (#637).
  • Support for transforms in Multi-Task GP models (#681).
  • Posterior sampling based on Matheron's rule for Multi-Task GP models (#841).
• Various changes to simplify and streamline integration with Ax:
  • Handle non-block designs in TrainingData (#794).
  • Acquisition function input constructor registry (#788, #802, #845).
• Random Fourier Feature (RFF) utilties for fast (approximate) GP function sampling (#750).
• DelaunayPolytopeSampler for fast uniform sampling from (simple) polytopes (#741).
• Add evaluate method to ScalarizedObjective (#795).

• Handle the case when all features are fixed in optimize_acqf (#770).
• Pass fixed_features to initial candidate generation functions (#806).
• Handle batch empty pareto frontier in FastPartitioning (#740).
• Handle empty pareto set in is_non_dominated (#743).
• Handle edge case of no or a single observation in get_chebyshev_scalarization (#762).
• Fix an issue in gen_candidates_torch that caused problems with acqusition functions using fantasy models (#766).
• Fix HigherOrderGP dtype bug (#728).
• Normalize before clamping in Warp input warping transform (#722).
• Fix bug in GP sampling (#764).

• Modify input transforms to support one-to-many transforms (#819, #835).
• Make initial conditions for acquisition function optimization honor parameter constraints (#752).
• Perform optimization only over unfixed features if fixed_features is passed (#839).
• Refactor Max Value Entropy Search Methods (#734).
• Use Linear Algebra functions from the torch.linalg module (#735).
• Use PyTorch's Kumaraswamy distribution (#746).
• Improved capabilities and some bugfixes for batched models (#723, #767).
• Pass callback argument to scipy.optim.minimize in gen_candidates_scipy (#744).
• Modify behavior of X_pending in in multi-objective acqusiition functions (#747).
• Allow multi-dimensional batch shapes in test functions (#757).
• Utility for converting batched multi-output models into batched single-output models (#759).
• Explicitly raise NotPSDError in _scipy_objective_and_grad (#787).
• Make raw_samples optional if batch_initial_conditions is passed (#801).
• Use powers of 2 in qMC docstrings & examples (#812).

• Require PyTorch >=1.7.1 (#714).
• Require GPyTorch >=1.4 (#714).

• HigherOrderGP - High-Order Gaussian Process (HOGP) model for high-dimensional output regression (#631, #646, #648, #680).
• qMultiStepLookahead acquisition function for general look-ahead optimization approaches (#611, #659).
• ScalarizedPosteriorMean and project_to_sample_points for more advanced MFKG functionality (#645).
• Large-scale Thompson sampling tutorial (#654, #713).
• Tutorial for optimizing mixed continuous/discrete domains (application to multi-fidelity KG with discrete fidelities) (#716).
• GPDraw utility for sampling from (exact) GP priors (#655).
• Add X as optional arg to call signature of MCAcqusitionObjective (#487).
• OSY synthetic test problem (#679).

• Fix matrix multiplication in scalarize_posterior (#638).
• Set X_pending in get_acquisition_function in qEHVI (#662).
• Make contextual kernel device-aware (#666).
• Do not use an MCSampler in MaxPosteriorSampling (#701).
• Add ability to subset outcome transforms (#711).

• Batchify box decomposition for 2d case (#642).

• Use scipy distribution in MES quantile bisect (#633).
• Use new closure definition for GPyTorch priors (#634).
• Allow enabling of approximate root decomposition in posterior calls (#652).
• Support for upcoming 21201-dimensional PyTorch SobolEngine (#672, #674).
• Refactored various MOO utilities to allow future additions (#656, #657, #658, #661).
• Support input_transform in PairwiseGP (#632).
• Output shape checks for t_batch_mode_transform (#577).
• Check for NaN in gen_candidates_scipy (#688).
• Introduce base_sample_shape property to Posterior objects (#718).

Contextual Bayesian Optimization, Input Warping, TuRBO, sampling from polytopes.

• Require PyTorch >=1.7 (#614).
• Require GPyTorch >=1.3 (#614).

• Models (LCE-A, LCE-M and SAC ) for Contextual Bayesian Optimziation (#581).
  • Implements core models from: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. Q. Feng, B. Letham, H. Mao, E. Bakshy. NeurIPS 2020.
  • See Ax for usage of these models.
• Hit and run sampler for uniform sampling from a polytope (#592).
• Input warping:
  • Core functionality (#607).
  • Kumaraswamy Distribution (#606).
  • Tutorial (8f34871652042219c57b799669a679aab5eed7e3).
• TuRBO-1 tutorial (#598).
  • Implements the method from Scalable Global Optimization via Local Bayesian Optimization. D. Eriksson, M. Pearce, J. Gardner, R. D. Turner, M. Poloczek. NeurIPS 2019.

• Fix bounds of HolderTable synthetic function (#596).
• Fix device issue in MOO tutorial (#621).

• Add train_inputs option to qMaxValueEntropy (#593).
• Enable gpytorch settings to override BoTorch defaults for fast_pred_var and debug (#595).
• Rename set_train_data_transform -> preprocess_transform (#575).
• Modify _expand_bounds() shape checks to work with >2-dim bounds (#604).
• Add batch_shape property to models (#588).
• Modify qMultiFidelityKnowledgeGradient.evaluate() to work with project, expand and cost_aware_utility (#594).
• Add list of papers using BoTorch to website docs (#617).

Maintenance Release

• Add PenalizedAcquisitionFunction wrapper (#585)
• Input transforms
  • Reversible input transform (#550)
  • Rounding input transform (#562)
  • Log input transform (#563)
• Differentiable approximate rounding for integers (#561)

• Fix sign error in UCB when maximize=False (a4bfacbfb2109d3b89107d171d2101e1995822bb)
• Fix batch_range sample shape logic (#574)

• Better support for two stage sampling in preference learning (0cd13d0cb49b1ac8d0971e42f1f0e9dd6126fd9a)
• Remove noise term in PairwiseGP and add ScaleKernel by default (#571)
• Rename prior to task_covar_prior in MultiTaskGP and FixedNoiseMultiTaskGP (8e42ea82856b165a7df9db2a9b6f43ebd7328fc4)
• Support only transforming inputs on training or evaluation (#551)
• Add equals method for InputTransform (#552)

Maintenance Release

• Constrained Multi-Objective tutorial (#493)
• Multi-fidelity Knowledge Gradient tutorial (#509)
• Support for batch qMC sampling (#510)
• New evaluate method for qKnowledgeGradient (#515)

• Require PyTorch >=1.6 (#535)
• Require GPyTorch >=1.2 (#535)
• Remove deprecated botorch.gen module (#532)

• Fix bad backward-indexing of task_feature in MultiTaskGP (#485)
• Fix bounds in constrained Branin-Currin test function (#491)
• Fix max_hv for C2DTLZ2 and make Hypervolume always return a float (#494)
• Fix bug in draw_sobol_samples that did not use the proper effective dimension (#505)
• Fix constraints for q>1 in qExpectedHypervolumeImprovement (c80c4fdb0f83f0e4f12e4ec4090d0478b1a8b532)
• Only use feasible observations in partitioning for qExpectedHypervolumeImprovement in get_acquisition_function (#523)
• Improved GPU compatibility for PairwiseGP (#537)

• Reduce memory footprint in qExpectedHypervolumeImprovement (#522)
• Add (q)ExpectedHypervolumeImprovement to nonnegative functions [for better initialization] (#496)

• Support batched best_f in qExpectedImprovement (#487)
• Allow to return full tree of solutions in OneShotAcquisitionFunction (#488)
• Added construct_inputs class method to models to programmatically construct the inputs to the constructor from a standardized TrainingData representation (#477, #482, 3621198d02195b723195b043e86738cd5c3b8e40)
• Acquisition function constructors now accept catch-all **kwargs options (#478, e5b69352954bb10df19a59efe9221a72932bfe6c)
• Use psd_safe_cholesky in qMaxValueEntropy for better numerical stabilty (#518)
• Added WeightedMCMultiOutputObjective (81d91fd2e115774e561c8282b724457233b6d49f)
• Add ability to specify outcomes to all multi-output objectives (#524)
• Return optimization output in info_dict for fit_gpytorch_scipy (#534)
• Use setuptools_scm for versioning (#539)

Multi-Objective Bayesian Optimization

• Multi-Objective Acquisition Functions (#466)
  • q-Expected Hypervolume Improvement
  • q-ParEGO
  • Analytic Expected Hypervolume Improvement with auto-differentiation
• Multi-Objective Utilities (#466)
  • Pareto Computation
  • Hypervolume Calculation
  • Box Decomposition algorithm
• Multi-Objective Test Functions (#466)
  • Suite of synthetic test functions for multi-objective, constrained optimization
• Multi-Objective Tutorial (#468)
• Abstract ConstrainedBaseTestProblem (#454)
• Add optimize_acqf_list method for sequentially, greedily optimizing 1 candidate from each provided acquisition function (d10aec911b241b208c59c192beb9e4d572a092cd)

• Fixed re-arranging mean in MultiTask MO models (#450).

• Move gpt_posterior_settings into models.utils (#449)
• Allow specifications of batch dims to collapse in samplers (#457)
• Remove outcome transform before model-fitting for sequential model fitting in MO models (#458)

Bugfix Release

• Fixed issue with broken wheel build (#444).

• Changed code style to use absolute imports throughout (#443).

Bugfix Release

• There was a mysterious issue with the 0.2.3 wheel on pypi, where part of the botorch/optim/utils.py file was not included, which resulted in an ImportError for many central components of the code. Interestingly, the source dist (built with the same command) did not have this issue.
• Preserve order in ChainedOutcomeTransform (#440).

• Utilities for estimating the feasible volume under outcome constraints (#437).

Pairwise GP for Preference Learning, Sampling Strategies.

• Require PyTorch >=1.5 (#423).
• Require GPyTorch >=1.1.1 (#425).

• Add PairwiseGP for preference learning with pair-wise comparison data (#388).
• Add SamplingStrategy abstraction for sampling-based generation strategies, including MaxPosteriorSampling (i.e. Thompson Sampling) and BoltzmannSampling (#218, #407).

• The existing botorch.gen module is moved to botorch.generation.gen and imports from botorch.gen will raise a warning (an error in the next release) (#218).

• Fix & update a number of tutorials (#394, #398, #393, #399, #403).
• Fix CUDA tests (#404).
• Fix sobol maxdim limitation in prune_baseline (#419).

• Better stopping criteria for stochastic optimization (#392).
• Improve numerical stability of LinearTruncatedFidelityKernel (#409).
• Allow batched best_f in qExpectedImprovement and qProbabilityOfImprovement (#411).
• Introduce new logger framework (#412).
• Faster indexing in some situations (#414).
• More generic BaseTestProblem (9e604fe2188ac85294c143d249872415c4d95823).

Require PyTorch 1.4, Python 3.7 and new features for active learning, multi-fidelity optimization, and a number of bug fixes.

• Require PyTorch >=1.4 (#379).
• Require Python >=3.7 (#378).

• Add qNegIntegratedPosteriorVariance for Bayesian active learning (#377).
• Add FixedNoiseMultiFidelityGP, analogous to SingleTaskMultiFidelityGP (#386).
• Support scalarize_posterior for m>1 and q>1 posteriors (#374).
• Support subset_output method on multi-fidelity models (#372).
• Add utilities for sampling from simplex and hypersphere (#369).

• Fix TestLoader local test discovery (#376).
• Fix batch-list conversion of SingleTaskMultiFidelityGP (#370).
• Validate tensor args before checking input scaling for more informative error messaages (#368).
• Fix flaky qNoisyExpectedImprovement test (#362).
• Fix test function in closed-loop tutorial (#360).
• Fix num_output attribute in BoTorch/Ax tutorial (#355).

• Require output dimension in MultiTaskGP (#383).
• Update code of conduct (#380).
• Remove deprecated joint_optimize and sequential_optimize (#363).

Minor bug fix release.

• Add a static method for getting batch shapes for batched MO models (#346).

• Revamp qKG constructor to avoid issue with missing objective (#351).
• Make sure MVES can support sampled costs like KG (#352).

• Allow custom module-to-array handling in fit_gpytorch_scipy (#341).

Max-value entropy acquisition function, cost-aware / multi-fidelity optimization, subsetting models, outcome transforms.

• Require PyTorch >=1.3.1 (#313).
• Require GPyTorch >=1.0 (#342).

• Add cost-aware KnowledgeGradient (qMultiFidelityKnowledgeGradient) for multi-fidelity optimization (#292).
• Add qMaxValueEntropy and qMultiFidelityMaxValueEntropy max-value entropy search acquisition functions (#298).
• Add subset_output functionality to (most) models (#324).
• Add outcome transforms and input transforms (#321).
• Add outcome_transform kwarg to model constructors for automatic outcome transformation and un-transformation (#327).
• Add cost-aware utilities for cost-sensitive acquisiiton functions (#289).
• Add DeterminsticModel and DetermisticPosterior abstractions (#288).
• Add AffineFidelityCostModel (f838eacb4258f570c3086d7cbd9aa3cf9ce67904).
• Add project_to_target_fidelity and expand_trace_observations utilties for use in multi-fidelity optimization (1ca12ac0736e39939fff650cae617680c1a16933).

• New prune_baseline option for pruning X_baseline in qNoisyExpectedImprovement (#287).
• Do not use approximate MLL computation for deterministic fitting (#314).
• Avoid re-evaluating the acquisition function in gen_candidates_torch (#319).
• Use CPU where possible in gen_batch_initial_conditions to avoid memory issues on the GPU (#323).

• Properly register NoiseModelAddedLossTerm in HeteroskedasticSingleTaskGP (671c93a203b03ef03592ce322209fc5e71f23a74).
• Fix batch mode for MultiTaskGPyTorchModel (#316).
• Honor propagate_grads argument in fantasize of FixedNoiseGP (#303).
• Properly handle diag arg in LinearTruncatedFidelityKernel (#320).

• Consolidate and simplify multi-fidelity models (#308).
• New license header style (#309).
• Validate shape of best_f in qExpectedImprovement (#299).
• Support specifying observation noise explicitly for all models (#256).
• Add num_outputs property to the Model API (#330).
• Validate output shape of models upon instantiating acquisition functions (#331).

• Silence warnings outside of explicit tests (#290).
• Enforce full sphinx docs coverage in CI (#294).

Knowledge Gradient acquisition function (one-shot), various maintenance

• Require explicit output dimensions in BoTorch models (#238)
• Make joint_optimize / sequential_optimize return acquisition function values (#149) [note deprecation notice below]
• standardize now works on the second to last dimension (#263)
• Refactor synthetic test functions (#273)

• Add qKnowledgeGradient acquisition function (#272, #276)
• Add input scaling check to standard models (#267)
• Add cyclic_optimize, convergence criterion class (#269)
• Add settings.debug context manager (#242)

• Consolidate sequential_optimize and joint_optimize into optimize_acqf (#150)

• Properly pass noise levels to GPs using a FixedNoiseGaussianLikelihood (#241) [requires gpytorch > 0.3.5]
• Fix q-batch dimension issue in ConstrainedExpectedImprovement (6c067185f56d3a244c4093393b8a97388fb1c0b3)
• Fix parameter constraint issues on GPU (#260)

• Add decorator for concatenating pending points (#240)
• Draw independent sample from prior for each hyperparameter (#244)
• Allow dim > 1111 for gen_batch_initial_conditions (#249)
• Allow optimize_acqf to use q>1 for AnalyticAcquisitionFunction (#257)
• Allow excluding parameters in fit functions (#259)
• Track the final iteration objective value in fit_gpytorch_scipy (#258)
• Error out on unexpected dims in parameter constraint generation (#270)
• Compute acquisition values in gen_ functions w/o grad (#274)

• Introduce BotorchTestCase to simplify test code (#243)
• Refactor tests to have monolithic cuda tests (#261)

Compatibility & maintenance release

• Updates to support breaking changes in PyTorch to boolean masks and tensor comparisons (#224).
• Require PyTorch >=1.2 (#225).
• Require GPyTorch >=0.3.5 (itself a compatibility release).

• Add FixedFeatureAcquisitionFunction wrapper that simplifies optimizing acquisition functions over a subset of input features (#219).
• Add ScalarizedObjective for scalarizing posteriors (#210).
• Change default optimization behavior to use L-BFGS-B by for box constraints (#207).

• Add validation to candidate generation (#213), making sure constraints are strictly satisfied (rater than just up to numerical accuracy of the optimizer).

• Introduce AcquisitionObjective base class (#220).
• Add propagate_grads context manager, replacing the propagate_grads kwarg in model posterior() calls (#221)
• Add batch_initial_conditions argument to joint_optimize() for warm-starting the optimization (ec3365a37ed02319e0d2bb9bea03aee89b7d9caa).
• Add return_best_only argument to joint_optimize() (#216). Useful for implementing advanced warm-starting procedures.

Maintenance release

• Avoid [PyTorch bug]((pytorch/pytorch#22353) resulting in bad gradients on GPU by requiring GPyTorch >= 0.3.4
• Fixes to resampling behavior in MCSamplers (#204)

• Linear truncated kernel for multi-fidelity bayesian optimization (#192)
• SingleTaskMultiFidelityGP for GP models that have fidelity parameters (#181)

API updates, more robust model fitting

• rename botorch.qmc to botorch.sampling, move MC samplers from acquisition.sampler to botorch.sampling.samplers (#172)

• Add condition_on_observations and fantasize to the Model level API (#173)
• Support pending observations generically for all MCAcqusitionFunctions (#176)
• Add fidelity kernel for training iterations/training data points (#178)
• Support for optimization constraints across q-batches (to support things like sample budget constraints) (2a95a6c3f80e751d5cf8bc7240ca9f5b1529ec5b)
• Add ModelList <-> Batched Model converter (#187)
• New test functions
  • basic: neg_ackley, cosine8, neg_levy, neg_rosenbrock, neg_shekel (e26dc7576c7bf5fa2ba4cb8fbcf45849b95d324b)
  • for multi-fidelity BO: neg_aug_branin, neg_aug_hartmann6, neg_aug_rosenbrock (ec4aca744f65ca19847dc368f9fee4cc297533da)

• More robust model fitting
  • Catch gpytorch numerical issues and return NaN to the optimizer (#184)
  • Restart optimization upon failure by sampling hyperparameters from their prior (#188)
  • Sequentially fit batched and ModelListGP models by default (#189)
  • Change minimum inferred noise level (e2c64fef1e76d526a33951c5eb75ac38d5581257)
• Introduce optional batch limit in joint_optimize to increases scalability of parallel optimization (baab5786e8eaec02d37a511df04442471c632f8a)
• Change constructor of ModelListGP to comply with GPyTorch’s IndependentModelList constructor (a6cf739e769c75319a67c7525a023ece8806b15d)
• Use torch.random to set default seed for samplers (rather than random) to making sampling reproducible when setting torch.manual_seed (ae507ad97255d35f02c878f50ba68a2e27017815)

• Use einsum in LinearMCObjective (22ca29535717cda0fcf7493a43bdf3dda324c22d)
• Change default Sobol sample size for MCAquisitionFunctions to be base-2 for better MC integration performance (5d8e81866a23d6bfe4158f8c9b30ea14dd82e032)
• Add ability to fit models in SumMarginalLogLikelihood sequentially (and make that the default setting) (#183)
• Do not construct the full covariance matrix when computing posterior of single-output BatchedMultiOutputGPyTorchModel (#185)

• Properly handle observation_noise kwarg for BatchedMultiOutputGPyTorchModels (#182)
• Fix a issue where f_best was always max for NoisyExpectedImprovement (de8544a75b58873c449b41840a335f6732754c77)
• Fix bug and numerical issues in initialize_q_batch (844dcd1dc8f418ae42639e211c6bb8e31a75d8bf)
• Fix numerical issues with inv_transform for qMC sampling (#162)

• Bump GPyTorch minimum requirement to 0.3.3

First public beta release.