Maintenance Release

New Features

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

Compatibility

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

Bug fixes

• 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 (c80c4fd)
• Only use feasible observations in partitioning for qExpectedHypervolumeImprovement
  in get_acquisition_function (#523)
• Improved GPU compatibility for PairwiseGP (#537)

Performance Improvements

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

Other changes

• 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, 3621198)
• Acquisition function constructors now accept catch-all **kwargs options
  (#478, e5b6935)
• Use psd_safe_cholesky in qMaxValueEntropy for better numerical stabilty (#518)
• Added WeightedMCMultiOutputObjective (81d91fd)
• 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)