pub struct MoreThuenteLineSearch<P, G, F> { /* private fields */ }
Expand description

The More-Thuente line search is a method which finds an appropriate step length from a starting point and a search direction. This point obeys the strong Wolfe conditions.

With the method with_c the scaling factors for the sufficient decrease condition and the curvature condition can be supplied. By default they are set to c1 = 1e-4 and c2 = 0.9.

Bounds on the range where step lengths are being searched for can be set with with_bounds which accepts a lower and an upper bound. Both values need to be non-negative and lower < upper.

One of the reasons for the algorithm to terminate is when the the relative width of the uncertainty interval is smaller than a given tolerance (default: 1e-10). This tolerance can be set via with_width_tolerance and must be non-negative.

TODO: Add missing stopping criteria!

Requirements on the optimization problem

The optimization problem is required to implement CostFunction and Gradient.

References

This implementation follows the excellent MATLAB implementation of Dianne P. O’Leary at http://www.cs.umd.edu/users/oleary/software/

Jorge J. More and David J. Thuente. “Line search algorithms with guaranteed sufficient decrease.” ACM Trans. Math. Softw. 20, 3 (September 1994), 286-307. DOI: https://doi.org/10.1145/192115.192132

Implementations§

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impl<P, G, F> MoreThuenteLineSearch<P, G, F>
where F: ArgminFloat,

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pub fn new() -> Self

Construct a new instance of MoreThuenteLineSearch

Example
let mtls: MoreThuenteLineSearch<Vec<f64>, Vec<f64>, f64> = MoreThuenteLineSearch::new();
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pub fn with_c(self, c1: F, c2: F) -> Result<Self, Error>

Set the constants c1 and c2 for the sufficient decrease and curvature conditions, respectively. 0 < c1 < c2 < 1 must hold.

The default values are c1 = 1e-4 and c2 = 0.9.

Example
let mtls: MoreThuenteLineSearch<Vec<f64>, Vec<f64>, f64> =
    MoreThuenteLineSearch::new().with_c(1e-3, 0.8)?;
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pub fn with_bounds(self, step_min: F, step_max: F) -> Result<Self, Error>

Set lower and upper bound of step

Defaults are step_min = sqrt(EPS) and step_max = INF.

step_min must be smaller than step_max.

Example
let mtls: MoreThuenteLineSearch<Vec<f64>, Vec<f64>, f64> =
    MoreThuenteLineSearch::new().with_bounds(1e-6, 10.0)?;
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pub fn with_width_tolerance(self, xtol: F) -> Result<Self, Error>

Set relative tolerance on width of uncertainty interval

The algorithm terminates when the relative width of the uncertainty interval is below the supplied tolerance.

Must be non-negative and defaults to 1e-10.

Example
let mtls: MoreThuenteLineSearch<Vec<f64>, Vec<f64>, f64> =
    MoreThuenteLineSearch::new().with_width_tolerance(1e-9)?;

Trait Implementations§

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impl<P: Clone, G: Clone, F: Clone> Clone for MoreThuenteLineSearch<P, G, F>

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fn clone(&self) -> MoreThuenteLineSearch<P, G, F>

Returns a copy of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl<P, G, F> Default for MoreThuenteLineSearch<P, G, F>
where F: ArgminFloat,

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fn default() -> Self

Returns the “default value” for a type. Read more
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impl<'de, P, G, F> Deserialize<'de> for MoreThuenteLineSearch<P, G, F>
where P: Deserialize<'de>, G: Deserialize<'de>, F: Deserialize<'de>,

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

Deserialize this value from the given Serde deserializer. Read more
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impl<P, G, F> LineSearch<G, F> for MoreThuenteLineSearch<P, G, F>
where F: ArgminFloat,

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fn search_direction(&mut self, search_direction: G)

Set search direction

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fn initial_step_length(&mut self, alpha: F) -> Result<(), Error>

Set initial alpha value

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impl<P, G, F> Serialize for MoreThuenteLineSearch<P, G, F>
where P: Serialize, G: Serialize, F: Serialize,

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

Serialize this value into the given Serde serializer. Read more
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impl<P, G, O, F> Solver<O, IterState<P, G, (), (), (), F>> for MoreThuenteLineSearch<P, G, F>
where O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G>, P: Clone + ArgminDot<G, F> + ArgminScaledAdd<G, F, P>, G: Clone + ArgminDot<G, F>, F: ArgminFloat,

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const NAME: &'static str = "More-Thuente Line search"

Name of the solver. Mainly used in Observers.
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fn init( &mut self, problem: &mut Problem<O>, state: IterState<P, G, (), (), (), F> ) -> Result<(IterState<P, G, (), (), (), F>, Option<KV>), Error>

Initializes the algorithm. Read more
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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.
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fn terminate_internal(&mut self, state: &I) -> TerminationStatus

Checks whether basic termination reasons apply. Read more
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fn terminate(&mut self, _state: &I) -> TerminationStatus

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

Auto Trait Implementations§

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impl<P, G, F> RefUnwindSafe for MoreThuenteLineSearch<P, G, F>

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impl<P, G, F> Send for MoreThuenteLineSearch<P, G, F>
where F: Send, G: Send, P: Send,

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impl<P, G, F> Sync for MoreThuenteLineSearch<P, G, F>
where F: Sync, G: Sync, P: Sync,

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impl<P, G, F> Unpin for MoreThuenteLineSearch<P, G, F>
where F: Unpin, G: Unpin, P: Unpin,

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impl<P, G, F> UnwindSafe for MoreThuenteLineSearch<P, G, F>
where F: UnwindSafe, G: UnwindSafe, P: UnwindSafe,

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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into<U> for T
where U: From<T>,

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> Same for T

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type Output = T

Should always be Self
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impl<SS, SP> SupersetOf<SS> for SP
where SS: SubsetOf<SP>,

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fn to_subset(&self) -> Option<SS>

The inverse inclusion map: attempts to construct self from the equivalent element of its superset. Read more
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fn is_in_subset(&self) -> bool

Checks if self is actually part of its subset T (and can be converted to it).
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fn to_subset_unchecked(&self) -> SS

Use with care! Same as self.to_subset but without any property checks. Always succeeds.
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fn from_subset(element: &SS) -> SP

The inclusion map: converts self to the equivalent element of its superset.
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impl<T> ToOwned for T
where T: Clone,

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<V, T> VZip<V> for T
where V: MultiLane<T>,

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fn vzip(self) -> V

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impl<T> DeserializeOwned for T
where T: for<'de> Deserialize<'de>,

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impl<T> SendAlias for T

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impl<T> SyncAlias for T