pub struct Steihaug<P, F> { /* private fields */ }
Expand description
The Steihaug method is a conjugate gradients based approach for finding an approximate solution to the second order approximation of the cost function within the trust region.
§References:
[0] Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 0-387-30303-0.
Implementations§
Trait Implementations§
Source§impl<P: Clone + Serialize, F: ArgminFloat> ArgminTrustRegion<F> for Steihaug<P, F>
impl<P: Clone + Serialize, F: ArgminFloat> ArgminTrustRegion<F> for Steihaug<P, F>
Source§fn set_radius(&mut self, radius: F)
fn set_radius(&mut self, radius: F)
Set the initial step length
Source§impl<'de, P, F> Deserialize<'de> for Steihaug<P, F>where
P: Deserialize<'de>,
F: Deserialize<'de>,
impl<'de, P, F> Deserialize<'de> for Steihaug<P, F>where
P: Deserialize<'de>,
F: Deserialize<'de>,
Source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
Source§impl<P: PartialOrd, F: PartialOrd> PartialOrd for Steihaug<P, F>
impl<P: PartialOrd, F: PartialOrd> PartialOrd for Steihaug<P, F>
Source§impl<P, O, F> Solver<O> for Steihaug<P, F>where
O: ArgminOp<Param = P, Output = F, Float = F>,
P: Clone + Serialize + DeserializeOwned + Default + ArgminMul<F, P> + ArgminWeightedDot<P, F, O::Hessian> + ArgminNorm<F> + ArgminDot<P, F> + ArgminAdd<P, P> + ArgminSub<P, P> + ArgminZeroLike,
O::Hessian: ArgminDot<P, P>,
F: ArgminFloat,
impl<P, O, F> Solver<O> for Steihaug<P, F>where
O: ArgminOp<Param = P, Output = F, Float = F>,
P: Clone + Serialize + DeserializeOwned + Default + ArgminMul<F, P> + ArgminWeightedDot<P, F, O::Hessian> + ArgminNorm<F> + ArgminDot<P, F> + ArgminAdd<P, P> + ArgminSub<P, P> + ArgminZeroLike,
O::Hessian: ArgminDot<P, P>,
F: ArgminFloat,
Source§fn init(
&mut self,
_op: &mut OpWrapper<O>,
state: &IterState<O>,
) -> Result<Option<ArgminIterData<O>>, Error>
fn init( &mut self, _op: &mut OpWrapper<O>, state: &IterState<O>, ) -> Result<Option<ArgminIterData<O>>, Error>
Initializes the algorithm Read more
Source§fn next_iter(
&mut self,
_op: &mut OpWrapper<O>,
state: &IterState<O>,
) -> Result<ArgminIterData<O>, Error>
fn next_iter( &mut self, _op: &mut OpWrapper<O>, state: &IterState<O>, ) -> Result<ArgminIterData<O>, Error>
Computes one iteration of the algorithm.
Source§fn terminate(&mut self, state: &IterState<O>) -> TerminationReason
fn terminate(&mut self, state: &IterState<O>) -> TerminationReason
Checks whether the algorithm must be terminated
Source§fn terminate_internal(&mut self, state: &IterState<O>) -> TerminationReason
fn terminate_internal(&mut self, state: &IterState<O>) -> TerminationReason
Checks whether basic termination reasons apply. Read more
impl<P: Copy, F: Copy> Copy for Steihaug<P, F>
impl<P, F> StructuralPartialEq for Steihaug<P, F>
Auto Trait Implementations§
impl<P, F> Freeze for Steihaug<P, F>
impl<P, F> RefUnwindSafe for Steihaug<P, F>where
F: RefUnwindSafe,
P: RefUnwindSafe,
impl<P, F> Send for Steihaug<P, F>
impl<P, F> Sync for Steihaug<P, F>
impl<P, F> Unpin for Steihaug<P, F>
impl<P, F> UnwindSafe for Steihaug<P, F>where
F: UnwindSafe,
P: UnwindSafe,
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more