Struct argmin::solver::quasinewton::SR1

pub struct SR1<L, F> { /* private fields */ }

Symmetric rank-one (SR1) method

This method currently has problems: https://github.com/argmin-rs/argmin/issues/221.

Requirements on the optimization problem

The optimization problem is required to implement CostFunction and Gradient.

Reference

Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 0-387-30303-0.

Implementations

impl<L, F> SR1<L, F>where
    F: ArgminFloat,

pub fn new(linesearch: L) -> Self

Construct a new instance of SR1

Example

let sr1: SR1<_, f64> = SR1::new(linesearch);

pub fn with_denominator_factor(
    self,
    denominator_factor: F
) -> Result<Self, Error>

Set denominator factor

If the denominator of the update is below the denominator_factor (scaled with other factors derived from the parameter vectors and the gradients), then the update of the inverse Hessian will be skipped.

Must be in (0, 1) and defaults to 1e-8.

Example

let sr1: SR1<_, f64> = SR1::new(linesearch).with_denominator_factor(1e-7)?;

pub fn with_tolerance_grad(self, tol_grad: F) -> Result<Self, Error>

The algorithm stops if the norm of the gradient is below tol_grad.

The provided value must be non-negative. Defaults to sqrt(EPSILON).

Example

let sr1: SR1<_, f64> = SR1::new(linesearch).with_tolerance_grad(1e-6)?;

pub fn with_tolerance_cost(self, tol_cost: F) -> Result<Self, Error>

Sets tolerance for the stopping criterion based on the change of the cost stopping criterion

The provided value must be non-negative. Defaults to EPSILON.

Example

let sr1: SR1<_, f64> = SR1::new(linesearch).with_tolerance_cost(1e-6)?;

Trait Implementations

impl<L: Clone, F: Clone> Clone for SR1<L, F>

fn clone(&self) -> SR1<L, F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'de, L, F> Deserialize<'de> for SR1<L, F>where
    L: Deserialize<'de>,
    F: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
    __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<L, F> Serialize for SR1<L, F>where
    L: Serialize,
    F: Serialize,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where
    __S: Serializer,

Serialize this value into the given Serde serializer.

impl<O, L, P, G, H, F> Solver<O, IterState<P, G, (), H, F>> for SR1<L, F>where
    O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G>,
    P: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminSub<P, P> + ArgminDot<G, F> + ArgminDot<P, F> + ArgminDot<P, H> + ArgminL2Norm<F> + ArgminMul<F, P>,
    G: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminSub<P, P> + ArgminL2Norm<F> + ArgminSub<G, G>,
    H: SerializeAlias + DeserializeOwnedAlias + ArgminDot<G, P> + ArgminDot<P, P> + ArgminAdd<H, H> + ArgminMul<F, H>,
    L: Clone + LineSearch<P, F> + Solver<O, IterState<P, G, (), (), F>>,
    F: ArgminFloat,

const NAME: &'static str = "SR1"

Name of the solver. Mainly used in Observers.

fn init(
    &mut self,
    problem: &mut Problem<O>,
    state: IterState<P, G, (), H, F>
) -> Result<(IterState<P, G, (), H, F>, Option<KV>), Error>

Initializes the algorithm.

fn next_iter(
    &mut self,
    problem: &mut Problem<O>,
    state: IterState<P, G, (), H, F>
) -> Result<(IterState<P, G, (), H, F>, Option<KV>), Error>

Computes a single iteration of the algorithm and has access to the optimization problem definition and the internal state of the solver. Returns an updated state and optionally a KV which holds key-value pairs used in Observers.

fn terminate(&mut self, state: &IterState<P, G, (), H, F>) -> TerminationStatus

Used to implement stopping criteria, in particular criteria which are not covered by (terminate_internal.

fn terminate_internal(&mut self, state: &I) -> TerminationStatus

Checks whether basic termination reasons apply.