Fix matrix Jacobians and SciPy optimizer behavior - #101
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Enable matrix Jacobians and sampled LQR coverage. Correct fixed-time event sensitivities and make SciPy CG reject unsupported bounds and constraints. Signed-off-by: mendax0110 <adrian.goessl@outlook.com>
Signed-off-by: mendax0110 <adrian.goessl@outlook.com>
Signed-off-by: mendax0110 <adrian.goessl@outlook.com>
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Summary
This PR fixes the matrix Jacobian ordering in the CasADi backend, corrects fixed-time event sensitivities and makes SciPy CG fail clearly when asked to handle unsupported bounds or constraints. It also updates the bounded time-switch example to use SLSQP.
Changes
Matrix Jacobians:
Support Jacobians with matrix-valued inputs in the CasADi backend. Reorded derivative columns to match Condor’s matrix-input representation (the previously skipped matrix-Jacobian test is now enabled)
Fixed-time event sensitivities:
Include the event function’s time derivative in the event-time sensitivity denominator. This then corrects sensitivities for explicitly timed events while preserving the existing state-triggered event behavior.
SciPy optimizer handling:
Stop passing bounds to SciPy when all variables are unbounded. Raises a clear error if CG is selected for a problem with variable bounds or constraints.
Examples and coverage:
Change the bounded time-switch example to use SLSQP. Added sampled-LQR coverage that optimizes the gain and checks it against the discrete algebraic Riccati solution.
Implementation ifc:
Aligns the SciPy construct method signature with the base class and clarify how exact_hessian is handled.