Examples

Examples:

• Basic tour of the Bayesian Optimization package
  • 1. Specifying the function to be optimized
  • 2. Getting Started
  • 3. Guiding the optimization
  • 4. Saving, loading and restarting
  • Next Steps
• Advanced tour of the Bayesian Optimization package
  • 1. Suggest-Evaluate-Register Paradigm
  • 2. Dealing with discrete parameters
  • 3. Tuning the underlying Gaussian Process
  • Observers Continued
• Exploitation vs Exploration
  • Target function
  • Acquisition Function “Upper Confidence Bound”
  • Acquisition Function “Expected Improvement”
  • Acquisition Function “Probability of Improvement”
• Visualization
  • Create a BayesianOptimization Object
  • Plotting and visualizing the algorithm at each step
  • Stopping
• Constrained Optimization
  • 1. Simple Constrained Optimization
  • 2. Advanced Constrained Optimization
  • 2.1 Simple, single constraint
  • Simulation 2
• Sequential Domain Reduction
  • Background
  • Basics
  • Parameters