Post-process an optimization problem#

Customize with matplotlib

Customize with matplotlib

Post-process a scenario

Post-process a scenario

Post-process an HDF5 file.

Post-process an HDF5 file.

Post-process an optimization problem

Post-process an optimization problem

Save a scenario for post-processing

Save a scenario for post-processing

Save an optimization problem for post-processing

Save an optimization problem for post-processing

Algorithms#

The data used to illustrate the post-processing algorithms in the following examples are from an MDO scenario on the Sobieski's SSBJ problem. The scenario uses the MDF formulation and was solved using the SciPy SLSQP algorithm. The data have been saved in an HDF5 file, which is passed to the high-level function execute_post() along with the appropriate settings model.

Note

To get the specific settings of a given post-processing, one should use the appropriate settings model accessible in gemseo.settings.post. Further details on the available post-processings and their settings can be found in the following dedicated page: Post-processing algorithms.

Details on how to execute such a scenario can be found here.

Basic history

Basic history

Constraints history

Constraints history

Correlations

Correlations

Gradient Sensitivity

Gradient Sensitivity

Objective and constraints history

Objective and constraints history

Optimization History View

Optimization History View

Parallel coordinates

Parallel coordinates

Pareto front

Pareto front

Pareto front on a multi-objective problem

Pareto front on a multi-objective problem

Quadratic approximations

Quadratic approximations

Radar chart

Radar chart

Robustness

Robustness

Scatter plot matrix

Scatter plot matrix

Self-Organizing Map

Self-Organizing Map

Variables influence

Variables influence

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