.. _Tutorials:

Tutorials 
=========


Optimization of scalar functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


:ref:`Standard Bayesian optimization <vanilla_bayesian_optimization>`         
  The target of the study is to run a standard Bayesian optimization of a scalar function.
  

:Driver: :ref:`BayesianOptimization`
  
         


:ref:`Global minimization of a non-expensive scalar function <differential_evolution>`         
  The target of the study is to run a global minimization a non-expensive scalar function without known gradient.
  

:Driver: :ref:`DifferentialEvolution`
  
         


:ref:`Global minimization of a non-expensive scalar function with discrete parameters <cma_es>`         
  The target of the study is to run a global minimization a non-expensive scalar function without known gradient on a mixed continuous and discrete design space.
  

:Driver: :ref:`CMAES`
  
         


:ref:`Gradient-based minimization of a non-expensive scalar function <scipy_minimization>`         
  The target of the study is to run a minimization a scalar function subject to inequality constraints based on the scipy implementation of the SLSQP method.
  

:Driver: :ref:`ScipyMinimizer`
  
         


.. toctree::
   :maxdepth: 100
   :hidden:


   vanilla_bayesian_optimization

   differential_evolution

   cma_es

   scipy_minimization




Least-squares optimization
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


:ref:`Solution of least-square problem using Bayesian optimization <bayesian_least_squares>`         
  The target of the study is solve a non-linear least-squares problem based on the Bayesian optimization approach.
  

:Driver: :ref:`BayesianLeastSquares`
  
         


:ref:`Solution of a non-expensive least-square problem based on a scipy implementation <scipy_least_squares>`         
  The target of the study is solve a non-linear least-squares problem based on the scipy implementation of the Trust Region Reflective algorithm.
  

:Driver: :ref:`ScipyLeastSquares`
  
         


:ref:`Bayesian parameter reconstruction using Bayesian optimization <bayesian_reconstruction>`         
  The target of the study is solve the Bayesian parameter reconstrution problem based on the Bayesian optimization approach.
  

:Driver: :ref:`BayesianReconstruction`
  
         


.. toctree::
   :maxdepth: 100
   :hidden:


   bayesian_least_squares

   scipy_least_squares

   bayesian_reconstruction




Advanced Topics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


:ref:`Optimal control of a system in a changing environment <changing_environment>`         
  The target of the study is show how to control a system that depends on environment parameters such as temperature or humidity.
  

:Driver: :ref:`ActiveLearning`
  
         


:ref:`Optimization of resonant system based on Gaussian fit <harmonic_oscillator_fit>`         
  The target of the study is to tune a resonant system using a Gaussian fit of the amplitude as a function of the resonance wavelength. The study showcases the use of a fit variable.
  

:Driver: :ref:`ActiveLearning`
  
         


:ref:`Benchmarking different studies against each other <benchmark>`         
  Different minimization studies are benchmarked against each other for a specific objective function.
  

:Drivers: :ref:`BayesianOptimization`, :ref:`ActiveLearning`, :ref:`CMAES`, :ref:`DifferentialEvolution`, :ref:`ScipyMinimizer`
  
         


:ref:`Variance-based sensitivity analysis for parameter reconstruction <sensitivity_analysis>`         
  A variance-based sensitivity analysis gives insights into how much information each measurement channel contributes for a parameter reconstruction.
  

:Driver: :ref:`ActiveLearning`
  
         


:ref:`Multi-objective optimization <multi_objective_optimization>`         
  A multi-objective optimization allows to identify the trade-off between different objective values.
  

:Driver: :ref:`ActiveLearning`
  
         


:ref:`Active learning of a global surrogate model <active_surrogate_training>`         
  The target of the study is to train a surrogate model of a vectorial function in an active-learning loop.
  

:Driver: :ref:`ActiveLearning`
  
         


.. toctree::
   :maxdepth: 100
   :hidden:


   changing_environment

   harmonic_oscillator_fit

   benchmark

   sensitivity_analysis

   multi_objective_optimization

   active_surrogate_training