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

Trait Implementations

impl TNLP for CountingTnlp

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_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 get_variables_linearity(&mut self, _types: &mut [Linearity]) -> bool

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

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

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