0.6.0

• gp: Kriging derivatives predictions are implemented (#44, #45), derivatives for Gp with linear regression are implemented (#47)
  • predict_derivatives: prediction of the output derivatives y wtr the input x
  • predict_variance_derivatives: prediction of the derivatives of the output variance wrt the input x
• moe: as above derivatives for smooth and hard predictions are implemented (#46)
• ego: when available derivatives are used to optimize the infill criterion with slsqp (#44)
• egobox Python binding: add GpMix/Gpx in Python egobox module, the Python binding of egobox-moe::Moe (#31)

0.5.0

• Enable Egor optimizer interruption with Ctrl+C (#30)
• API improvements: generalize the use of ArrayBase<...>, better linfa integration by implementing PredictInplace trait (#37)
• Minor performance improvement in mixture of experts clustering (#29)
• Documentation improvements: JOSS paper submission review (#34, #36, #38, #39, #40, #42)

0.4.0

• Generate Python egobox module for Linux (#20)
• Improve Egor robustness by adding LHS optimization (#21)
• Improve moe with automatic number of clusters determination (#22)
• Use linfa 0.6.0 making BLAS dependency optional (#23)
• Improve Egor by implementing automatic reclustering every 10-points addition (#25)
• Fix Egor parallel infill strategy (qEI): bad objectives and constraints gp models update (#26)

0.3.0

• Refactor sub-packages to follow linfa conventions
• Improve naming consistency
• Update documentation

0.2.1

• Improve documentation
• egobox Python module: rename egobox Optimizer class to Egor

0.2.0

New features:

• Add hot start
• Add constraint handling
• Add mixed-integer optimization capability
• Add Python binding with PyO3

0.1.0

Initial version contains:

• doe: LHS, FullFactorial, Random sampling
• gp: Gaussian Process models with 3 regression models (constant, linear quadratic) and 4 correlation models (squared exponential, absolute exponential, matern32, matern52)
• moe: Mixture of Experts: find the bests mix of gps given a number of clusters regarding smooth or hard recombination
• ego: Contains egor optimizer which is a super EGO algorithm implemented on top of the previous elements.
  It implements several infill strategy: EI, WB2, WB2S and use either COBYLA or SLSQP for internal optimization.