[WIP] Improvements to the dynamic optimization interface#4394
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SebastianM-C wants to merge 13 commits intoSciML:masterfrom
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[WIP] Improvements to the dynamic optimization interface#4394SebastianM-C wants to merge 13 commits intoSciML:masterfrom
SebastianM-C wants to merge 13 commits intoSciML:masterfrom
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Add `scale` metadata option for symbolic variables, following the same pattern as `bounds` and `guess`. Scales provide characteristic magnitudes used to normalize dynamics constraints in optimal control formulations, preventing ill-conditioning from variables with vastly different scales. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
FunctionWrapper parameters (e.g., interpolators) fail during fixpoint_sub with fold=Val(true) because they can't accept JuMP types. Register them as JuMP nonlinear operators instead, with automatic derivative detection via Symbolics' derivative_rule system. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Ensure that observed equations and constraints referencing pre-binding parameter names can be fully substituted by propagating parameter bindings into the substitution map. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
After mtkcompile, all declared inputs are bound in equations, making unbound_inputs return empty. For optimal control, all declared inputs should become control variables regardless of binding status. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Allow users to provide constraint scaling, variable bounds, and function-valued start trajectories at problem construction time. All keys are normalized via default_toterm for consistent lookup. Thread kwargs through all backend extensions (InfiniteOpt, CasADi, Pyomo). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace inequality-constraint-based bounds with JuMP set_lower_bound/ set_upper_bound via dispatched set_variable_bounds!. Extract bounds logic into extract_variable_bounds with support for user-provided bounds that override metadata. This gives Ipopt better barrier method structure for optimal control problems. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Allow users to provide function-valued initial trajectories for state variables via the initial_trajectory kwarg. Functions are applied via dispatched set_initial_trajectory! after variable creation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Formulate dynamics as (∂x - tₛ*f(x)) / scale == 0 instead of scaling each side independently. This prevents degenerate tₛ → 0 solutions and improves Ipopt convergence for problems with multi-scale state variables. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Substitute observed variables before parameters so that observed equations referencing parameters get fully resolved during fixpoint_sub. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Set print_level after set_optimizer to prevent the optimizer reset from discarding the attribute. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extend the optimal control bounds system to support observed variables: - `extract_variable_bounds` now returns `observed_bounds` from variable metadata and user `bounds` dict (user bounds take priority) - InfiniteOpt backend lifts observed bounds into auxiliary bounded decision variables with equality constraints, which interior-point solvers handle much more efficiently than nonlinear inequalities - Auxiliary variables get proper start values computed from the initial state, and equality constraints are scaled via `scales` dict or variable metadata - Change `add_initial_constraints!` from `fix` to `@constraint` to preserve variable bounds at the initial time point Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fixup! Add observed variable bounds lifting and fix initial constraints Remove aux variable start value computation. Setting start values from the initial state (e.g. power=15680) creates badly scaled initial points without improving convergence. The equality constraint (expr-aux)/s==0 drives aux to the correct value regardless of start. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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