2 What’s new in PEPit 0.2.0

  • Adding possibility to set LMI constraints associated to function objects.

    The method add_psd_matrix has been added to the class Function and must be used to add LMI constraints associated to a function.

  • Storing dual values prior to dimension reduction.

    Each Constraint object receives a dual value in the attribute _dual_value which can be accessed through the method eval_dual. In previous releases, and in case of dimension reduction being activated, the dual values being stored where those of the latest solved problem. From this release, the dual values being stored are always those of the original problem. Note the primal values are those of the last problem providing adversarial example of smallest dimension possible.

  • Creating PSDMatrix class.

    PSDMatrix class as been added. This doesn’t affect how the methods add_psd_matrix must be used. A user must continue providing a psd matrix under the form of an Iterable of Expression``s. The latter will be automatically transformed into a ``PSDMatrix object that contains a _dual_value attribute and an eval_dual method as any Constraint object.

  • Fixing a minor issue in pep.py.

    There was an issue when the Gram matrix G did not need any eigenvalue correction as eig_threshold in pep.get_nb_eigenvalues_and_corrected_matrix where defined as the maximum of an empty list. This issue has been fixed in this release.

  • Eigenvalues are now sorted in decreasing order in the output of the PEP, making it easier to plot low-dimensional worst-case examples (examples of such usages can be found in the exercise repository Learning-Performance-Estimation).

  • Many new examples were introduced, including for looking for low-dimensional worst-case examples, fixed-point iterations, variational inequalities, and continuous-time dynamics.