Struct MetropolisOptimizer 

pub struct MetropolisOptimizer { /* private fields */ }

Optimizer that implements the Metropolis algorithm with constant beta

Implementations

impl MetropolisOptimizer

pub fn new( patience: usize, n_trials: usize, return_iter: usize, beta: f64, ) -> Self

Constructor of MetropolisOptimizer

• patience : the optimizer will give up if there is no improvement of the score after this number of iterations
• n_trials : number of trial solutions to generate and evaluate at each iteration
• return_iter : returns to the best solution if there is no improvement after this number of iterations.
• beta : inverse temperature

pub fn step<M: OptModel<ScoreType = NotNan<f64>>>( &self, model: &M, initial_solution: M::SolutionType, initial_score: M::ScoreType, n_iter: usize, time_limit: Duration, callback: &mut dyn OptCallbackFn<M::SolutionType, M::ScoreType>, ) -> StepResult<M::SolutionType, M::ScoreType>

Perform one optimization step

Trait Implementations

impl Clone for MetropolisOptimizer

fn clone(&self) -> MetropolisOptimizer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for MetropolisOptimizer

fn optimize( &self, model: &M, initial_solution: <M as OptModel>::SolutionType, initial_score: <M as OptModel>::ScoreType, n_iter: usize, time_limit: Duration, callback: &mut dyn OptCallbackFn<<M as OptModel>::SolutionType, <M as OptModel>::ScoreType>, ) -> (<M as OptModel>::SolutionType, <M as OptModel>::ScoreType)

Start optimization

• model : the model to optimize
• initial_solution : the initial solution to start optimization
• initial_score : the initial score of the initial solution
• n_iter: maximum iterations
• time_limit: maximum iteration time
• callback : callback function that will be invoked at the end of each iteration

fn run( &self, model: &M, initial_solution_and_score: Option<(M::SolutionType, M::ScoreType)>, n_iter: usize, time_limit: Duration, ) -> Result<(M::SolutionType, M::ScoreType), LocalsearchError>

generate initial solution if not given and run optimization

fn run_with_callback( &self, model: &M, initial_solution_and_score: Option<(M::SolutionType, M::ScoreType)>, n_iter: usize, time_limit: Duration, callback: &mut dyn OptCallbackFn<M::SolutionType, M::ScoreType>, ) -> Result<(M::SolutionType, M::ScoreType), LocalsearchError>

generate initial solution if not given and run optimization with callback

impl Copy for MetropolisOptimizer