Struct argmin::solver::gradientdescent::SteepestDescent

pub struct SteepestDescent<L> { /* private fields */ }

Steepest descent

Iteratively takes steps in the direction of the strongest negative gradient. In each iteration, a line search is used to obtain an appropriate step length.

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> SteepestDescent<L>

pub fn new(linesearch: L) -> Self

Construct a new instance of SteepestDescent

Requires a line search.

Example

let sd = SteepestDescent::new(linesearch);

Trait Implementations

impl<L: Clone> Clone for SteepestDescent<L>

fn clone(&self) -> SteepestDescent<L>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'de, L> Deserialize<'de> for SteepestDescent<L>where
    L: 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> Serialize for SteepestDescent<L>where
    L: 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, F> Solver<O, IterState<P, G, (), (), F>> for SteepestDescent<L>where
    O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G>,
    P: Clone + SerializeAlias + DeserializeOwnedAlias,
    G: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminMul<F, P>,
    L: Clone + LineSearch<P, F> + Solver<O, IterState<P, G, (), (), F>>,
    F: ArgminFloat,

const NAME: &'static str = "Steepest Descent"

Name of the solver. Mainly used in Observers.

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

Initializes the algorithm.

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

Checks whether basic termination reasons apply.

fn terminate(&mut self, _state: &I) -> TerminationStatus

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