Struct argmin::core::LinearProgramState

pub struct LinearProgramState<P, F> {

    pub param: Option<P>,
    pub prev_param: Option<P>,
    pub best_param: Option<P>,
    pub prev_best_param: Option<P>,
    pub cost: F,
    pub prev_cost: F,
    pub best_cost: F,
    pub prev_best_cost: F,
    pub target_cost: F,
    pub iter: u64,
    pub last_best_iter: u64,
    pub max_iters: u64,
    pub counts: HashMap<String, u64>,
    pub counting_enabled: bool,
    pub time: Option<Duration>,
    pub termination_status: TerminationStatus,
}

Maintains the state from iteration to iteration of a solver

This struct is passed from one iteration of an algorithm to the next.

Keeps track of

• parameter vector of current and previous iteration
• best parameter vector of current and previous iteration
• cost function value of current and previous iteration
• current and previous best cost function value
• target cost function value
• current iteration number
• iteration number where the last best parameter vector was found
• maximum number of iterations that will be executed
• problem function evaluation counts (cost function, gradient, jacobian, hessian,
• elapsed time
• termination status

Fields

param: Option<P>

Current parameter vector

prev_param: Option<P>

Previous parameter vector

best_param: Option<P>

Current best parameter vector

prev_best_param: Option<P>

Previous best parameter vector

cost: F

Current cost function value

prev_cost: F

Previous cost function value

best_cost: F

Current best cost function value

prev_best_cost: F

Previous best cost function value

target_cost: F

Target cost function value

iter: u64

Current iteration

last_best_iter: u64

Iteration number of last best cost

max_iters: u64

Maximum number of iterations

counts: HashMap<String, u64>

Evaluation counts

counting_enabled: bool

Update evaluation counts?

time: Option<Duration>

Time required so far

termination_status: TerminationStatus

Status of optimization execution

Implementations

impl<P, F> LinearProgramState<P, F>

pub fn param(self, param: P) -> Self

Set parameter vector. This shifts the stored parameter vector to the previous parameter vector.

Example

let state = state.param(param);

pub fn target_cost(self, target_cost: F) -> Self

Set target cost.

When this cost is reached, the algorithm will stop. The default is Self::Float::NEG_INFINITY.

Example

let state = state.target_cost(0.0);

pub fn max_iters(self, iters: u64) -> Self

Set maximum number of iterations

Example

let state = state.max_iters(1000);

pub fn cost(self, cost: F) -> Self

Set the current cost function value. This shifts the stored cost function value to the previous cost function value.

Example

let state = state.cost(cost);

pub fn counting(self, mode: bool) -> Self

Overrides state of counting function executions (default: false)

let state = state.counting(true);

Trait Implementations

impl<P: Clone, F: Clone> Clone for LinearProgramState<P, F>

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

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<P: Debug, F: Debug> Debug for LinearProgramState<P, F>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de, P, F> Deserialize<'de> for LinearProgramState<P, F>

where P: Deserialize<'de>, F: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<P, F> Serialize for LinearProgramState<P, F>

where P: Serialize, F: Serialize,

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<P, F> State for LinearProgramState<P, F>

where P: Clone, F: ArgminFloat,

type Param = P

Type of parameter vector

type Float = F

Floating point precision

fn new() -> Self

Create new LinearProgramState instance

Example

use argmin::core::{LinearProgramState, State};
let state: LinearProgramState<Vec<f64>, f64> = LinearProgramState::new();

fn update(&mut self)

Checks if the current parameter vector is better than the previous best parameter value. If a new best parameter vector was found, the state is updated accordingly.

Example

let mut state: LinearProgramState<Vec<f64>, f64> = LinearProgramState::new();

// Simulating a new, better parameter vector
state.best_param = Some(vec![1.0f64]);
state.best_cost = 10.0;
state.param = Some(vec![2.0f64]);
state.cost = 5.0;

// Calling update
state.update();

// Check if update was successful
assert_eq!(state.best_param.as_ref().unwrap()[0], 2.0f64);
assert_eq!(state.best_cost.to_ne_bytes(), state.best_cost.to_ne_bytes());
assert!(state.is_best());

For algorithms which do not compute the cost function, every new parameter vector will be the new best:

let mut state: LinearProgramState<Vec<f64>, f64> = LinearProgramState::new();

// Simulating a new, better parameter vector
state.best_param = Some(vec![1.0f64]);
state.param = Some(vec![2.0f64]);

// Calling update
state.update();

// Check if update was successful
assert_eq!(state.best_param.as_ref().unwrap()[0], 2.0f64);
assert_eq!(state.best_cost.to_ne_bytes(), state.best_cost.to_ne_bytes());
assert!(state.is_best());

fn get_param(&self) -> Option<&P>

Returns a reference to the current parameter vector

Example

let param = state.get_param();  // Option<&P>

fn get_best_param(&self) -> Option<&P>

Returns a reference to the current best parameter vector

Example

let best_param = state.get_best_param();  // Option<&P>

fn terminate_with(self, reason: TerminationReason) -> Self

Sets the termination status to Terminated with the given reason

Example

let state = state.terminate_with(TerminationReason::MaxItersReached);

fn time(&mut self, time: Option<Duration>) -> &mut Self

Sets the time required so far.

Example

let state = state.time(Some(instant::Duration::new(0, 12)));

fn get_cost(&self) -> Self::Float

Returns current cost function value.

Example

let cost = state.get_cost();

fn get_best_cost(&self) -> Self::Float

Returns current best cost function value.

Example

let best_cost = state.get_best_cost();

fn get_target_cost(&self) -> Self::Float

Returns target cost function value.

Example

let target_cost = state.get_target_cost();

fn get_iter(&self) -> u64

Returns current number of iterations.

Example

let iter = state.get_iter();

fn get_last_best_iter(&self) -> u64

Returns iteration number of last best parameter vector.

Example

let last_best_iter = state.get_last_best_iter();

fn get_max_iters(&self) -> u64

Returns the maximum number of iterations.

Example

let max_iters = state.get_max_iters();

fn get_termination_status(&self) -> &TerminationStatus

Returns the termination status.

Example

let termination_status = state.get_termination_status();

fn get_termination_reason(&self) -> Option<&TerminationReason>

Returns the termination reason if terminated, otherwise None.

Example

let termination_reason = state.get_termination_reason();

fn get_time(&self) -> Option<Duration>

Returns the time elapsed since the start of the optimization.

Example

let time = state.get_time();

fn increment_iter(&mut self)

Increments the number of iterations by one

Example

state.increment_iter();

fn func_counts<O>(&mut self, problem: &Problem<O>)

Set all function evaluation counts to the evaluation counts of another Problem.

state.func_counts(&problem);

fn get_func_counts(&self) -> &HashMap<String, u64>

Returns function evaluation counts

Example

let counts = state.get_func_counts();

fn is_best(&self) -> bool

Returns whether the current parameter vector is also the best parameter vector found so far.

Example

let is_best = state.is_best();

fn terminated(&self) -> bool

Return whether the algorithm has terminated or not