**Optimal Response A****nalysis**

*Topics*

1. | Introduction. Responses in two variables. | |

2. | Mathematics of response surfaces. | |

3. | Estimation procedures. | |

4. | Optimal response values in bounded regions. | |

5. | Optimal response and experimental design. | |

6. | Case study | |

7. | Evolutionary operations. | |

8. | Taguchi designs. | |

9. | Process optimizations. Examples | |

10. | Dimension analysis. | |

11. | Empirical procedures. | |

12. | Graphics analysis. | |

13. | Optimization in batch processes. | |

14. | Specialized procedures | |

15. | Case study. | |

16. | Comparisons of methods | |

17. | Confidence intervals | |

18. | Detection of special features | |

19. | Sensitivity analysis | |

20. | Case study | |

21. | Guidelines for presentation of results |

In industry there is great interes in process optimization. This can often be formulated as finding a maximum of a surface in a multi-dimensional space. If the mathematical model is formulated as a quadratic surface in the variables, the optimal response has a well-defined mathematical formulae. But in industrial applications it is often necessary to work with low rank data, where the estimation procedures breaks down. The application of the H-method provides with an alternative view on the task of optimality that utilizes the special features in data. In the idustrial applications it has provided with reliable solutions to the important task of process optimization in relation to different types of models.

See a short review of some ideas.