Struct ActivityReport 

pub struct ActivityReport {

    pub mu: Number,
    pub var_status: Vec<i8>,
    pub var_ratio: Vec<Number>,
    pub var_q_sign: Vec<i8>,
    pub var_off_central_path: Vec<bool>,
    pub var_contaminated: Vec<bool>,
    pub var_sigma: Vec<Number>,
    pub row_status: Vec<i8>,
    pub row_ratio: Vec<Number>,
    pub row_q_sign: Vec<i8>,
    pub row_off_central_path: Vec<bool>,
    pub row_contaminated: Vec<bool>,
    pub row_sigma: Vec<Number>,
}

Per-variable and per-row classification of a converged solve.

All vectors are user-space: var_* have length n_full_x (the user TNLP’s n) and row_* length n_full_g (the user’s m). Entries with no finite bound hold UNBOUNDED; FIXED variables and EQUALITY rows are placeholders for entries the barrier never classified. All three carry NaN ratios.

Fields

mu: Number

Barrier parameter of the converged iterate.

var_status: Vec<i8>

Status per user variable (codes above).

var_ratio: Vec<Number>

Σ_i / q_i per user variable; NaN where not classified. For an UNIDENTIFIED entry the value is Σ/floor, a lower bound on any honest ratio rather than the ratio itself, since q is below the identification floor there.

var_q_sign: Vec<i8>

Sign of the signed curvature H_ii (−1, 0, +1); the absolute value goes into q, so an indefinite direction is reported rather than hidden.

var_off_central_path: Vec<bool>

s·z differs from μ by more than a factor of ten on some side: off the central path, or the bound was relaxed.

var_contaminated: Vec<bool>

Classified inactive yet r non-negligible: barrier curvature where none should be.

var_sigma: Vec<Number>

The barrier diagonal Σ_i = z/s itself per user variable, both sides summed; 0 where not classified. In natural (unscaled) units, the repo’s sensitivity-output contract: classification runs on the solver’s scaled quantities (the ratio is scale-invariant), the report does not. The covariance roadmap’s item 1 subtracts exactly this from the factor’s natural-units reduced Hessian.

row_status: Vec<i8>

Status per user constraint row.

row_ratio: Vec<Number>

Σ_j / q_j per user row; NaN where not classified. UNIDENTIFIED entries hold Σ/floor as for variables.

row_q_sign: Vec<i8>

Sign of the signed row curvature ∇dⱼᵀ H ∇dⱼ.

row_off_central_path: Vec<bool>

Central-path check per row, as for variables.

row_contaminated: Vec<bool>

Contamination check per row, as for variables.

row_sigma: Vec<Number>

The row barrier diagonal Σ_j = v/s per user row, both sides summed; 0 where not classified. In natural (unscaled) units like Self::var_sigma, and RAW rather than the geometric weight the classification uses: item 1 restricts the normal to its own fitted block and applies its own ‖a‖² factor there.