Struct argmin::solver::linesearch::hagerzhang::HagerZhangLineSearch

pub struct HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>,  { /* fields omitted */ }

The Hager-Zhang line search is a method to find a step length which obeys the strong Wolfe conditions.

Example

// Define inital parameter vector
let init_param: Vec<f64> = vec![1.0, 0.0];

// Problem definition
let operator = MyProblem {};

// Set up line search method
let mut solver = HagerZhangLineSearch::new(operator);

// Set search direction
solver.set_search_direction(vec![-2.0, 0.0]);

// Set initial position
solver.set_initial_parameter(init_param);

// Calculate initial cost ...
solver.calc_initial_cost()?;
// ... or, alternatively, set cost if it is already computed
// solver.set_initial_cost(...);

// Calculate initial gradient ...
solver.calc_initial_gradient()?;
// .. or, alternatively, set gradient if it is already computed
// solver.set_initial_gradient(...);

// Set initial step length
solver.set_initial_alpha(1.0)?;

// Attach a logger
solver.add_logger(ArgminSlogLogger::term());

// Run solver
solver.run()?;

// Wait a second (lets the logger flush everything before printing again)
std::thread::sleep(std::time::Duration::from_secs(1));

// Print Result
println!("{:?}", solver.result());

References

[0] 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

Methods

impl<O> HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>, 

pub fn new(operator: O) -> Self

Constructor

Parameters:

operator: operator

pub fn set_cur_grad(&mut self, grad: O::Param) -> &mut Self

set current gradient value

pub fn set_delta(&mut self, delta: f64) -> Result<&mut Self, Error>

set delta

pub fn set_sigma(&mut self, sigma: f64) -> Result<&mut Self, Error>

set sigma

pub fn set_epsilon(&mut self, epsilon: f64) -> Result<&mut Self, Error>

set epsilon

pub fn set_theta(&mut self, theta: f64) -> Result<&mut Self, Error>

set theta

pub fn set_gamma(&mut self, gamma: f64) -> Result<&mut Self, Error>

set gamma

pub fn set_eta(&mut self, eta: f64) -> Result<&mut Self, Error>

set eta

pub fn set_alpha_min_max(
    &mut self,
    alpha_min: f64,
    alpha_max: f64
) -> Result<&mut Self, Error>

set alpha limits

Trait Implementations

impl<'de, O> Deserialize<'de> for HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>,
    O: Deserialize<'de>,
    O::Param: Deserialize<'de>,
    O::Param: Deserialize<'de>,
    O::Param: Deserialize<'de>, 

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

Deserialize this value from the given Serde deserializer.

impl<O> Serialize for HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>,
    O: Serialize,
    O::Param: Serialize,
    O::Param: Serialize,
    O::Param: 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> ArgminSolver for HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>, 

fn run(&mut self) -> Result<ArgminResult<Self::Param>, Error>

Run the optimization algorithm

fn run_fast(&mut self) -> Result<ArgminResult<Self::Param>, Error>

Run the essential parts of the optimization algorithm (no logging, no Ctrl-C handling)

fn apply(&mut self, param: &Self::Param) -> Result<Self::Output, Error>

Applies the cost function or operator to a parameter vector param. Returns an Err if apply of ArgminOperator is not implemented.

fn gradient(&mut self, param: &Self::Param) -> Result<Self::Param, Error>

Computes the gradient at parameter param. Returns an Err if gradient of ArgminOperator is not implemented.

fn hessian(&mut self, param: &Self::Param) -> Result<Self::Hessian, Error>

Computes the Hessian at parameter param. Returns an Err if hessian of ArgminOperator is not implemented.

fn cur_param(&self) -> Self::Param

Returns the current parameter vector.

fn cur_grad(&self) -> Self::Param

Returns the most recently stored gradient.

fn cur_hessian(&self) -> Self::Hessian

Returns the most recently stored Hessian.

fn set_cur_param(&mut self, param: Self::Param)

Sets the current parameter to param.

fn set_cur_grad(&mut self, grad: Self::Param)

Sets the current gradient to grad.

fn set_cur_hessian(&mut self, hessian: Self::Hessian)

Sets the current Hessian to hessian.

fn set_best_param(&mut self, param: Self::Param)

Sets the best parameter vector to param.

fn modify(&self, param: &Self::Param, factor: f64) -> Result<Self::Param, Error>

Modify the parameter vector by calling the modify method of the trait ArgminOperator. Will return an Err if modify is not implemented.

fn result(&self) -> ArgminResult<Self::Param>

Returns the result of the optimization.

fn set_max_iters(&mut self, iters: u64)

Sets the maximum number of iterations to iters.

fn max_iters(&self) -> u64

Returns the maximum number of iterations.

fn increment_iter(&mut self)

Increments the iteration counter.

fn cur_iter(&self) -> u64

Returns the current number of iterations.

fn cur_cost(&self) -> f64

Returns the most recently stored cost function value.

fn set_cur_cost(&mut self, cost: f64)

Sets the current cost function value to cost

fn best_cost(&self) -> f64

Returns the best cost function value obtained so far.

fn set_best_cost(&mut self, cost: f64)

Sets the best cost function value.

fn set_target_cost(&mut self, cost: f64)

Sets the target cost function value to cost. The optimization algorithm will be terminated when this limit is reached.

fn increment_cost_func_count(&mut self)

Increments the counter for the computations of the cost function by 1.

fn increase_cost_func_count(&mut self, count: u64)

Increases the counter for the computations of the cost function by count.

fn cost_func_count(&self) -> u64

Returns the current value of the counter for the computations of the cost function.

fn increment_grad_func_count(&mut self)

Increments the counter for the computations of the gradient by 1.

fn increase_grad_func_count(&mut self, count: u64)

Increases the counter for the computations of the gradient by count.

fn grad_func_count(&self) -> u64

Returns the current value of the counter for the computations of the gradient.

fn increment_hessian_func_count(&mut self)

Increments the counter for the computations of the Hessian by 1.

fn increase_hessian_func_count(&mut self, count: u64)

Increases the counter for the computations of the Hessian by count.

fn hessian_func_count(&self) -> u64

Returns the current value of the counter for the computations of the Hessian.

fn add_logger(&mut self, logger: Arc<dyn ArgminLog>)

Attaches a logger which implements ArgminLog to the solver.

fn add_writer(&mut self, writer: Arc<dyn ArgminWrite<Param = Self::Param>>)

Attaches a writer which implements ArgminWrite to the solver.

fn set_termination_reason(&mut self, reason: TerminationReason)

Sets the TerminationReason

fn terminate(&mut self) -> TerminationReason

Checks whether any of the conditions to terminate is true and terminates the algorithm.

fn base_reset(&mut self)

Resets the base field to it's initial conditions. This is helpful for implementing a solver which is initialized once, but called several times. It is recommended to only call this method inside the init function of ArgminNextIter.

fn set_checkpoint_dir(&mut self, dir: &str)

Set checkpoint directory

fn set_checkpoint_name(&mut self, name: &str)

Set checkpoint name

fn set_checkpoint_mode(&mut self, mode: CheckpointMode)

Set checkpoint mode

fn from_checkpoint<P>(path: P) -> Result<Self, Error> where
    P: AsRef<Path>,
    Self: DeserializeOwned, 

Load solver from checkpoint

impl<O> ArgminIter for HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>, 

type Param = O::Param

Parameter vectors

type Output = f64

Output of the operator

type Hessian = O::Hessian

Hessian

fn init(&mut self) -> Result<(), Error>

Initializes the algorithm

fn next_iter(&mut self) -> Result<ArgminIterData<Self::Param>, Error>

Computes one iteration of the algorithm.

impl<O> ArgminLineSearch for HagerZhangLineSearch<O> where
    O: ArgminOp<Output = f64>,
    O::Param: ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, f64> + ArgminScaledAdd<O::Param, f64, O::Param>, 

fn set_search_direction(&mut self, search_direction: O::Param)

Set search direction

fn set_initial_parameter(&mut self, param: O::Param)

Set initial parameter

fn set_initial_cost(&mut self, init_cost: f64)

Set initial cost function value

fn set_initial_gradient(&mut self, init_grad: O::Param)

Set initial gradient

fn calc_initial_cost(&mut self) -> Result<(), Error>

Calculate initial cost function value

fn calc_initial_gradient(&mut self) -> Result<(), Error>

Calculate initial cost function value

fn set_initial_alpha(&mut self, alpha: f64) -> Result<(), Error>

Set initial alpha value