Hypervolume Knowledge Gradient (HVKG)

New features

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).

Bug fixes

• 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).

Other changes

• 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.