Struct CountingTnlp 

pub struct CountingTnlp {
    pub n_obj: Cell<i32>,
    pub n_grad_f: Cell<i32>,
    pub n_g: Cell<i32>,
    pub n_jac_g: Cell<i32>,
    pub n_h: Cell<i32>,
    /* private fields */
}

Fields

n_obj: Cell<i32>

n_grad_f: Cell<i32>

n_g: Cell<i32>

n_jac_g: Cell<i32>

n_h: Cell<i32>

Implementations

impl CountingTnlp

pub fn new(inner: Rc<RefCell<dyn TNLP>>) -> Self

pub fn captured_solution(&self) -> Option<(Vec<Number>, Vec<Number>)>

The (x, lambda) captured at the last finalize_solution, if any.

pub fn captured_bound_mults(&self) -> Option<(Vec<Number>, Vec<Number>)>

The (z_l, z_u) captured at that same finalize_solution.

None, and empty vectors, are distinct: a non-OrigIpoptNlp model hands back empty bound-multiplier blocks and no suffixes are written, which callers must not confuse with “not captured”.

Trait Implementations

impl TNLP for CountingTnlp

fn derivative_proofs(&mut self) -> DerivativeProofs

Transparent decorator: forward the constant-derivative proofs, or OrigIpoptNlp asks this wrapper — which knows no algebra — and gets the declining default, so gh #588 Q6’s reuse never engages on any model the CLI solves. Neither wrapper changes a row, a variable or a derivative’s value, so both directions of the proof carry through unchanged.

fn presolve_infeasibility_proof(&self) -> Option<InfeasibilityProof>

Transparent decorator: forward the presolve infeasibility proof, or the application never sees it. The CLI stacks this counter above the presolve wrapper, so without this the proof is swallowed here and the solve runs anyway.

fn get_nlp_info(&mut self) -> Option<NlpInfo>

Required. Problem dimensions and triplet index style.

fn get_bounds_info(&mut self, b: BoundsInfo<'_>) -> bool

Required. Variable / constraint bounds.

fn get_starting_point(&mut self, sp: StartingPoint<'_>) -> bool

Required. Initial primal (and optionally dual) point.

fn eval_f(&mut self, x: &[Number], new_x: bool) -> Option<Number>

Required. Objective value at x.

fn eval_grad_f( &mut self, x: &[Number], new_x: bool, grad_f: &mut [Number], ) -> bool

Required. Objective gradient at x into grad_f.

fn eval_g(&mut self, x: &[Number], new_x: bool, g: &mut [Number]) -> bool

Required. Constraint values g(x).

fn eval_jac_g( &mut self, x: Option<&[Number]>, new_x: bool, mode: SparsityRequest<'_>, ) -> bool

Required. Jacobian of g. Sparsity vs. values selected by mode. x and new_x are unused on the structure call.

fn eval_h( &mut self, x: Option<&[Number]>, new_x: bool, obj_factor: Number, lambda: Option<&[Number]>, new_lambda: bool, mode: SparsityRequest<'_>, ) -> bool

Required for exact Hessian, optional for L-BFGS. Hessian of the Lagrangian. Default returns false (signals to %Ipopt that quasi-Newton must be used).

fn finalize_solution( &mut self, sol: Solution<'_>, ip_data: &IpoptData, ip_cq: &IpoptCq, )

Required. Receives the final iterate after solve.

fn get_var_con_metadata( &mut self, var: &mut MetaData, con: &mut MetaData, ) -> bool

Provide variable/constraint metadata (e.g. idx_names). Default: no metadata.

fn get_scaling_parameters(&mut self, req: ScalingRequest<'_>) -> bool

User-supplied scaling, used only when nlp_scaling_method=user-scaling. Default: declines.

fn get_variables_linearity(&mut self, types: &mut [Linearity]) -> bool

Variable linearity tags (used by Bonmin, not by Ipopt).

fn get_objective_variables_linearity(&mut self, types: &mut [Linearity]) -> bool

Per-variable linearity with respect to the objective only (a pounce extension; upstream has no objective-scoped query). NonLinear iff the objective’s nonlinear part depends on the variable; a variable that enters the objective only linearly (or not at all) is Linear even when it is nonlinear in a constraint. Consumed by presolve’s Phase-0 objective-coupling guard, which must not mistake constraint-only nonlinearity for objective coupling. Default: declines (slice untouched).

fn get_constraints_linearity(&mut self, types: &mut [Linearity]) -> bool

Constraint linearity tags. Used by adaptive-mu’s nlp_scaling_method=equilibration-based.

fn get_number_of_nonlinear_variables(&mut self) -> Index

Number of variables that appear nonlinearly. Returning -1 means “treat all as nonlinear” (the Ipopt default).

fn get_list_of_nonlinear_variables(&mut self, pos: &mut [Index]) -> bool

List of nonlinear variable indices, in the index style returned from Self::get_nlp_info.

fn intermediate_callback( &mut self, stats: IterStats, ip_data: &IpoptData, ip_cq: &IpoptCq, ) -> bool

Per-iteration intermediate callback. Returning false requests early termination with User_Requested_Stop.

fn finalize_metadata(&mut self, var: &MetaData, con: &MetaData)

Final metadata pass — called just before Self::finalize_solution. Default does nothing.

fn is_presolve_wrapper(&self) -> bool

Whether this TNLP is already an explicit generic-presolve wrapper.

fn scaling_factors(&self) -> Option<Vec<f64>>

The per-variable scaling factors this decorator applies, if it is a scaling wrapper (gh#486). Consumers that read the algorithm’s iterate rather than the finalize_solution payload see scaled coordinates and need these to undo the substitution. A transparent decorator should forward the inner answer.