Struct SqpQpData 

pub struct SqpQpData {
    pub n: usize,
    pub m: usize,
    pub h: SymTMatrix,
    pub g: Vec<Number>,
    pub a: GenTMatrix,
    pub bl: Vec<Number>,
    pub bu: Vec<Number>,
    pub xl: Vec<Number>,
    pub xu: Vec<Number>,
    pub hessian_inertia: HessianInertia,
}

Owned linearization data for a single SQP iteration.

Fields

n: usize

m: usize

h: SymTMatrix

g: Vec<Number>

a: GenTMatrix

bl: Vec<Number>

bu: Vec<Number>

xl: Vec<Number>

xu: Vec<Number>

hessian_inertia: HessianInertia

Implementations

impl SqpQpData

pub fn build( x: &[Number], grad_f: &[Number], c_vals: &[Number], bl_c: &[Number], bu_c: &[Number], xl_orig: &[Number], xu_orig: &[Number], jac_c: Triplet, hess_lag: Triplet, hessian_inertia: HessianInertia, ) -> Self

Assemble from concrete linearization arrays at iterate x.

• grad_f — ∇f(x), length n.
• c_vals — c(x), length m (may contain inequality slack values too; convention is bl_c[i] ≤ c[i] ≤ bu_c[i]).
• bl_c, bu_c — original NLP constraint bounds.
• xl_orig, xu_orig — original NLP variable bounds.
• jac_c — ∇c(x) triplet, m × n.
• hess_lag — ∇²L(x, λ_g) triplet, n × n symmetric.

pub fn as_qp(&self) -> QpProblem<'_>

Borrowed QpProblem view ready for pounce_qp::QpSolver::solve.