Struct argmin::solver::linesearch::HagerZhangLineSearch
source · [−]pub struct HagerZhangLineSearch<P, G, F> { /* private fields */ }
Expand description
Hager-Zhang line search
The Hager-Zhang line search is a method to find a step length which obeys the strong Wolfe conditions.
Requirements on the optimization problem
The optimization problem is required to implement CostFunction
and Gradient
.
Reference
William W. Hager and Hongchao Zhang. “A new conjugate gradient method with guaranteed descent and an efficient line search.” SIAM J. Optim. 16(1), 2006, 170-192. DOI: https://doi.org/10.1137/030601880
Implementations
sourceimpl<P, G, F> HagerZhangLineSearch<P, G, F> where
P: ArgminScaledAdd<P, F, P> + ArgminDot<G, F>,
F: ArgminFloat,
impl<P, G, F> HagerZhangLineSearch<P, G, F> where
P: ArgminScaledAdd<P, F, P> + ArgminDot<G, F>,
F: ArgminFloat,
sourcepub fn new() -> Self
pub fn new() -> Self
Construct a new instance of HagerZhangLineSearch
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> = HagerZhangLineSearch::new();
sourcepub fn with_delta_sigma(self, delta: F, sigma: F) -> Result<Self, Error>
pub fn with_delta_sigma(self, delta: F, sigma: F) -> Result<Self, Error>
Set delta and sigma.
Delta defaults to 0.1
and must be in (0, 1)
.
Sigma defaults to 0.9
and must be in [delta, 1)
.
Delta and Sigma correspond to the constants c1
and c2
of the strong Wolfe conditions,
respectively.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_delta_sigma(0.2, 0.8)?;
sourcepub fn with_epsilon(self, epsilon: F) -> Result<Self, Error>
pub fn with_epsilon(self, epsilon: F) -> Result<Self, Error>
Set epsilon
Used in the approximate strong Wolfe condition.
Must be non-negative and defaults to 1e-6
.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_epsilon(1e-8)?;
sourcepub fn with_theta(self, theta: F) -> Result<Self, Error>
pub fn with_theta(self, theta: F) -> Result<Self, Error>
Set theta
Used in the update rules when the potential intervals [a, c] or [c, b] violate the opposite slope condition.
Must be in (0, 1)
and defaults to 0.5
.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_theta(0.4)?;
sourcepub fn with_gamma(self, gamma: F) -> Result<Self, Error>
pub fn with_gamma(self, gamma: F) -> Result<Self, Error>
Set gamma
Determines when a bisection step is performed.
Must be in (0, 1)
and defaults to 0.66
.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_gamma(0.7)?;
sourcepub fn with_eta(self, eta: F) -> Result<Self, Error>
pub fn with_eta(self, eta: F) -> Result<Self, Error>
Set eta
Used in the lower bound for beta_k^N
.
Must be larger than zero and defaults to 0.01
.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_eta(0.02)?;
sourcepub fn with_bounds(self, step_min: F, step_max: F) -> Result<Self, Error>
pub fn with_bounds(self, step_min: F, step_max: F) -> Result<Self, Error>
Set lower and upper bound of step
Defaults to a minimum step length of EPSILON
and a maximum step length of 1e5
.
The chosen values must satisfy 0 <= step_min < step_max
.
Example
let hzls: HagerZhangLineSearch<Vec<f64>, Vec<f64>, f64> =
HagerZhangLineSearch::new().with_bounds(1e-3, 1.0)?;
Trait Implementations
sourceimpl<P: Clone, G: Clone, F: Clone> Clone for HagerZhangLineSearch<P, G, F>
impl<P: Clone, G: Clone, F: Clone> Clone for HagerZhangLineSearch<P, G, F>
sourcefn clone(&self) -> HagerZhangLineSearch<P, G, F>
fn clone(&self) -> HagerZhangLineSearch<P, G, F>
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl<P, G, F> Default for HagerZhangLineSearch<P, G, F> where
P: ArgminScaledAdd<P, F, P> + ArgminDot<G, F>,
F: ArgminFloat,
impl<P, G, F> Default for HagerZhangLineSearch<P, G, F> where
P: ArgminScaledAdd<P, F, P> + ArgminDot<G, F>,
F: ArgminFloat,
sourceimpl<'de, P, G, F> Deserialize<'de> for HagerZhangLineSearch<P, G, F> where
P: Deserialize<'de>,
G: Deserialize<'de>,
F: Deserialize<'de>,
impl<'de, P, G, F> Deserialize<'de> for HagerZhangLineSearch<P, G, F> where
P: Deserialize<'de>,
G: Deserialize<'de>,
F: Deserialize<'de>,
sourcefn 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
sourceimpl<P, G, F> LineSearch<P, F> for HagerZhangLineSearch<P, G, F>
impl<P, G, F> LineSearch<P, F> for HagerZhangLineSearch<P, G, F>
sourcefn search_direction(&mut self, search_direction: P)
fn search_direction(&mut self, search_direction: P)
Set search direction
sourcefn initial_step_length(&mut self, alpha: F) -> Result<(), Error>
fn initial_step_length(&mut self, alpha: F) -> Result<(), Error>
Set initial alpha value
sourceimpl<P, G, F> Serialize for HagerZhangLineSearch<P, G, F> where
P: Serialize,
G: Serialize,
F: Serialize,
impl<P, G, F> Serialize for HagerZhangLineSearch<P, G, F> where
P: Serialize,
G: Serialize,
F: Serialize,
sourceimpl<P, G, O, F> Solver<O, IterState<P, G, (), (), F>> for HagerZhangLineSearch<P, G, F> where
O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G>,
P: Clone + SerializeAlias + ArgminDot<G, F> + ArgminScaledAdd<P, F, P>,
G: Clone + SerializeAlias + ArgminDot<P, F>,
F: ArgminFloat,
impl<P, G, O, F> Solver<O, IterState<P, G, (), (), F>> for HagerZhangLineSearch<P, G, F> where
O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G>,
P: Clone + SerializeAlias + ArgminDot<G, F> + ArgminScaledAdd<P, F, P>,
G: Clone + SerializeAlias + ArgminDot<P, F>,
F: ArgminFloat,
sourceconst NAME: &'static str = "Hager-Zhang line search"
const NAME: &'static str = "Hager-Zhang line search"
Name of the solver. Mainly used in Observers.
sourcefn init(
&mut self,
problem: &mut Problem<O>,
state: IterState<P, G, (), (), F>
) -> Result<(IterState<P, G, (), (), F>, Option<KV>), Error>
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
sourcefn next_iter(
&mut self,
problem: &mut Problem<O>,
state: IterState<P, G, (), (), F>
) -> Result<(IterState<P, G, (), (), F>, Option<KV>), Error>
fn next_iter(
&mut self,
problem: &mut Problem<O>,
state: IterState<P, G, (), (), F>
) -> Result<(IterState<P, G, (), (), F>, Option<KV>), Error>
sourcefn terminate(&mut self, _state: &IterState<P, G, (), (), F>) -> TerminationReason
fn terminate(&mut self, _state: &IterState<P, G, (), (), F>) -> TerminationReason
Used to implement stopping criteria, in particular criteria which are not covered by
(terminate_internal
. Read more
sourcefn terminate_internal(&mut self, state: &I) -> TerminationReason
fn terminate_internal(&mut self, state: &I) -> TerminationReason
Checks whether basic termination reasons apply. Read more
Auto Trait Implementations
impl<P, G, F> RefUnwindSafe for HagerZhangLineSearch<P, G, F> where
F: RefUnwindSafe,
G: RefUnwindSafe,
P: RefUnwindSafe,
impl<P, G, F> Send for HagerZhangLineSearch<P, G, F> where
F: Send,
G: Send,
P: Send,
impl<P, G, F> Sync for HagerZhangLineSearch<P, G, F> where
F: Sync,
G: Sync,
P: Sync,
impl<P, G, F> Unpin for HagerZhangLineSearch<P, G, F> where
F: Unpin,
G: Unpin,
P: Unpin,
impl<P, G, F> UnwindSafe for HagerZhangLineSearch<P, G, F> where
F: UnwindSafe,
G: UnwindSafe,
P: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more