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§
source§impl<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§
source§impl<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>
source§fn clone(&self) -> HagerZhangLineSearch<P, G, F>
fn clone(&self) -> HagerZhangLineSearch<P, G, F>
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl<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,
source§impl<'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>,
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>,
source§impl<P, G, F> LineSearch<P, F> for HagerZhangLineSearch<P, G, F>
impl<P, G, F> LineSearch<P, F> for HagerZhangLineSearch<P, G, F>
source§fn search_direction(&mut self, search_direction: P)
fn search_direction(&mut self, search_direction: P)
Set search direction
source§impl<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,
source§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,
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,
source§const NAME: &'static str = "Hager-Zhang line search"
const NAME: &'static str = "Hager-Zhang line search"
source§fn 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>
source§fn 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>
state
and optionally a KV
which holds key-value pairs used in
Observers.source§fn terminate(&mut self, _state: &IterState<P, G, (), (), F>) -> TerminationStatus
fn terminate(&mut self, _state: &IterState<P, G, (), (), F>) -> TerminationStatus
terminate_internal
. Read more