Struct ADProblem 

pub struct ADProblem<T, const X: usize, const CACHE: bool> { /* private fields */ }

Wrapper struct that handles caching and sparsity detection to interface between the blackbox model and IPOPT.

Implementations

impl<T: SimpleADProblem<X>, const X: usize> ADProblem<T, X, false>

pub fn new(problem: T) -> Self

Initialize an NLP using the SimpleADProblem interface.

impl<T: CachedADProblem<X>, const X: usize> ADProblem<T, X, true>

pub fn new_cached(problem: T) -> Result<Self, T::Error>

Initialize an NLP using the CachedADProblem interface.

Trait Implementations

impl<T: SimpleADProblem<X>, const X: usize> BasicProblem for ADProblem<T, X, false>

fn num_variables(&self) -> usize

Total number of variables of the non-linear problem.

fn bounds(&self, x_l: &mut [f64], x_u: &mut [f64]) -> bool

Specify lower and upper variable bounds given by x_l and x_u respectively.

fn initial_point(&self, x: &mut [f64]) -> bool

Construct the initial guess of the primal variables for Ipopt to start with.

fn objective(&self, x: &[f64], _: bool, obj: &mut f64) -> bool

Objective function. This is the function being minimized.

fn objective_grad(&self, x: &[f64], _: bool, grad_f: &mut [f64]) -> bool

The gradient of the objective function speficies the search direction to Ipopt.

fn indexing_style(&self) -> IndexingStyle

Specify the indexing style used for arrays in this problem. (Default is zero-based)

fn initial_bounds_multipliers(&self, z_l: &mut [f64], z_u: &mut [f64]) -> bool

Construct the initial guess of the lower and upper bounds multipliers for Ipopt to start with.

fn variable_scaling(&self, _x_scaling: &mut [f64]) -> bool

Provide custom variable scaling.

fn objective_scaling(&self) -> f64

Provide custom scaling for the objective function.

impl<T: CachedADProblem<X>, const X: usize> BasicProblem for ADProblem<T, X, true>

fn num_variables(&self) -> usize

Total number of variables of the non-linear problem.

fn bounds(&self, x_l: &mut [f64], x_u: &mut [f64]) -> bool

Specify lower and upper variable bounds given by x_l and x_u respectively.

fn initial_point(&self, x: &mut [f64]) -> bool

Construct the initial guess of the primal variables for Ipopt to start with.

fn objective(&self, x: &[f64], new_x: bool, obj: &mut f64) -> bool

Objective function. This is the function being minimized.

fn objective_grad(&self, x: &[f64], new_x: bool, grad_f: &mut [f64]) -> bool

The gradient of the objective function speficies the search direction to Ipopt.

fn indexing_style(&self) -> IndexingStyle

Specify the indexing style used for arrays in this problem. (Default is zero-based)

fn initial_bounds_multipliers(&self, z_l: &mut [f64], z_u: &mut [f64]) -> bool

Construct the initial guess of the lower and upper bounds multipliers for Ipopt to start with.

fn variable_scaling(&self, _x_scaling: &mut [f64]) -> bool

Provide custom variable scaling.

fn objective_scaling(&self) -> f64

Provide custom scaling for the objective function.

impl<T: SimpleADProblem<X>, const X: usize> ConstrainedProblem for ADProblem<T, X, false>

fn num_constraints(&self) -> usize

Number of equality and inequality constraints.

fn num_constraint_jacobian_non_zeros(&self) -> usize

Number of non-zeros in the constraint Jacobian.

fn constraint(&self, x: &[f64], _: bool, g: &mut [f64]) -> bool

Constraint function.

fn constraint_bounds(&self, g_l: &mut [f64], g_u: &mut [f64]) -> bool

Specify lower and upper bounds, g_l and g_u respectively, on the constraint function.

fn constraint_jacobian_indices( &self, rows: &mut [i32], cols: &mut [i32], ) -> bool

Constraint Jacobian indices.

fn constraint_jacobian_values( &self, x: &[f64], _: bool, vals: &mut [f64], ) -> bool

Constraint Jacobian values.

fn num_hessian_non_zeros(&self) -> usize

Number of non-zeros in the Hessian matrix.

fn hessian_indices(&self, rows: &mut [i32], cols: &mut [i32]) -> bool

Hessian indices.

fn hessian_values( &self, x: &[f64], _new_x: bool, obj_factor: f64, lambda: &[f64], vals: &mut [f64], ) -> bool

Hessian values.

fn initial_constraint_multipliers(&self, lambda: &mut [f64]) -> bool

Construct the initial guess of the constraint multipliers for Ipopt to start with.

fn constraint_scaling(&self, _g_scaling: &mut [f64]) -> bool

Provide custom constraint function scaling.

impl<T: CachedADProblem<X>, const X: usize> ConstrainedProblem for ADProblem<T, X, true>

fn num_constraints(&self) -> usize

Number of equality and inequality constraints.

fn num_constraint_jacobian_non_zeros(&self) -> usize

Number of non-zeros in the constraint Jacobian.

fn constraint(&self, x: &[f64], new_x: bool, g: &mut [f64]) -> bool

Constraint function.

fn constraint_bounds(&self, g_l: &mut [f64], g_u: &mut [f64]) -> bool

Specify lower and upper bounds, g_l and g_u respectively, on the constraint function.

fn constraint_jacobian_indices( &self, rows: &mut [i32], cols: &mut [i32], ) -> bool

Constraint Jacobian indices.

fn constraint_jacobian_values( &self, x: &[f64], new_x: bool, vals: &mut [f64], ) -> bool

Constraint Jacobian values.

fn num_hessian_non_zeros(&self) -> usize

Number of non-zeros in the Hessian matrix.

fn hessian_indices(&self, rows: &mut [i32], cols: &mut [i32]) -> bool

Hessian indices.

fn hessian_values( &self, x: &[f64], _new_x: bool, obj_factor: f64, lambda: &[f64], vals: &mut [f64], ) -> bool

Hessian values.

fn initial_constraint_multipliers(&self, lambda: &mut [f64]) -> bool

Construct the initial guess of the constraint multipliers for Ipopt to start with.

fn constraint_scaling(&self, _g_scaling: &mut [f64]) -> bool

Provide custom constraint function scaling.