Is there a way to specify objective and constraint gradients when doing nonlinear optimization in the problem-based framework?
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In the solver-based framework, user-applied gradients are specified as additional output arguments from the objective and constraint functions. However, in the problem-based framwork, the objective and constraints are specified as symbolic expressions, not function handles. So, the concept of additional outputs does not seem applicable. Is there a way to to supply analytical gradients and Hessians in problem-based implementations?
Alan Weiss on 9 Aug 2022
The problem-based approach calculates and uses gradients automatically for supported functions. However, to include gradients of unsupported functions or to include a Hessian, see Supply Derivatives in Problem-Based Workflow.
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