Compatibility and Maintenance release

Compatibility

• 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).

New Features

• 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).

Bug fixes

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

Minor changes

• 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 (ec3365a).
• Add return_best_only argument to joint_optimize() (#216). Useful for
  implementing advanced warm-starting procedures.