While most plugins do query the full constraint Jacobian through impl_jacobian, CFSQP can and does query single rows of the Jacobian matrix (e.g. gradient w.r.t. the i-th parameter for the j-th output of the k-th constraint). As a result, the plugin relies on proper impl_gradient implementations.
In practice, for complex multidimensional constraints, it is often easier to provide a single impl_jacobian method, and in this case, it is common for users to implement an empty impl_gradient (pure virtual method), and CFSQP fails since it gets a null vector, leaving the user confused.
Thus, we should clarify the guidelines for such cases (e.g. throw exceptions in the empty impl_gradient method), and provide helper classes/functions to detect such issues, for instance by comparing results from impl_jacobian and impl_gradient.
cc @stanislas-brossette
While most plugins do query the full constraint Jacobian through
impl_jacobian, CFSQP can and does query single rows of the Jacobian matrix (e.g. gradient w.r.t. thei-th parameter for thej-th output of thek-th constraint). As a result, the plugin relies on properimpl_gradientimplementations.In practice, for complex multidimensional constraints, it is often easier to provide a single
impl_jacobianmethod, and in this case, it is common for users to implement an emptyimpl_gradient(pure virtual method), and CFSQP fails since it gets a null vector, leaving the user confused.Thus, we should clarify the guidelines for such cases (e.g. throw exceptions in the empty
impl_gradientmethod), and provide helper classes/functions to detect such issues, for instance by comparing results fromimpl_jacobianandimpl_gradient.cc @stanislas-brossette