Struct argmin::solver::quasinewton::DFP

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

Davidon-Fletcher-Powell (DFP) method

The Davidon-Fletcher-Powell algorithm (DFP) is a method for solving unconstrained nonlinear optimization problems.

The algorithm requires a line search which is provided via the constructor. Additionally an initial guess for the parameter vector and an initial inverse Hessian is required, which are to be provided via the configure method of the Executor (See IterState, in particular IterState::param and IterState::inv_hessian). In the same way the initial gradient and cost function corresponding to the initial parameter vector can be provided. If these are not provided, they will be computed during initialization of the algorithm.

A tolerance on the gradient can be configured with with_tolerance_grad: If the norm of the gradient is below said tolerance, the algorithm stops. It defaults to sqrt(EPSILON).

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> DFP<L, F>

where F: ArgminFloat,

pub fn new(linesearch: L) -> Self

Construct a new instance of DFP

Example

let dfp: DFP<_, f64> = DFP::new(linesearch);

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 dfp: DFP<_, f64> = DFP::new(linesearch).with_tolerance_grad(1e-6)?;

Trait Implementations

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

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

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

fn name(&self) -> &str

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.