Struct LeastSquaresMinimum 

pub struct LeastSquaresMinimum {
    pub x: Vec<f64>,
    pub cost: f64,
    pub gradient_norm: f64,
    pub error_estimate: Option<f64>,
    pub jacobian_condition: f64,
    pub iterations: usize,
    pub evaluations: usize,
    pub termination: Termination,
    pub status: Status,
}

A local minimiser of 1/2 ||r(x)||^2.

Fields

x: Vec<f64>

The minimiser estimate.

cost: f64

1/2 ||r(x)||^2.

gradient_norm: f64

||J^T r||_inf at x.

error_estimate: Option<f64>

Length of the undamped Gauss-Newton step from x: near an isolated minimiser with a well-conditioned Jacobian, an estimate of the distance to it. Where jacobian_condition is large the iteration converges only linearly and this can underestimate the distance by a small factor. None where the Jacobian is rank-deficient at x, so the minimiser is not isolated and no estimate is given.

jacobian_condition: f64

Estimate of the Jacobian’s condition number at x.

iterations: usize

Iterations performed.

evaluations: usize

Residual evaluations performed (Jacobian evaluations not counted).

termination: Termination

Why it stopped.

status: Status

Status view of termination.

Implementations

impl LeastSquaresMinimum

pub fn converged(self) -> NumericResult<Self>

The minimum if a tolerance was met.

Errors

NumericError::NotConverged with the gradient norm reached.

Trait Implementations

impl Clone for LeastSquaresMinimum

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LeastSquaresMinimum

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for LeastSquaresMinimum

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for LeastSquaresMinimum