RsBundle  Check-in [8d6b894491]

pub mod minorant;
pub use minorant::Minorant;

pub mod firstorderproblem;
pub use firstorderproblem::{Evaluation, SimpleEvaluation, Update, FirstOrderProblem};

pub mod solver;
pub use solver::{Solver, SolverParams, BundleState, Terminator, StandardTerminator,
                 Weighter, Step, UpdateState, IterationInfo};

mod hkweighter;
pub use hkweighter::HKWeighter;

mod master;

pub mod mcf;

    /// The minorant's index in the master problem
    index: usize,
    /// Current multiplier.
    multiplier: Real,
    /// Primal associated with this minorant.
    primal: Option<Pr>,
}

/// Information about the last iteration.
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum IterationInfo {
    NewMinorantTooHigh{ new: Real, old: Real },
    UpperBoundNullStep,
    ShallowCut,
}


/// State information for the update callback.
pub struct UpdateState<'a, Pr:'a> {
    /// Current model minorants.
    minorants: &'a [Vec<MinorantInfo<Pr>>],
    /// The last step type.
    pub step: Step,
    /// Iteration information.
    pub iteration_info: &'a [IterationInfo],
}

impl<'a, Pr:'a> UpdateState<'a, Pr> {
    pub fn aggregated_primals(&self, subproblem : usize) -> Vec<(Real, &Pr)> {
        self.minorants[subproblem].iter().map(|m| {
            (m.multiplier, m.primal.as_ref().unwrap())
        }).collect()

    start_time : Instant,

    /// The master problem.
    master: Box<MasterProblem<MinorantIndex=usize>>,

    /// The active minorant indices for each subproblem.
    minorants: Vec<Vec<MinorantInfo<Pr>>>,

    /// Accumulated information about the last iteration.
    iterinfos: Vec<IterationInfo>,
}


impl<P, Pr, E> Solver<P, Pr, E>
    where P : for <'a> FirstOrderProblem<'a, Primal=Pr,EvalResult=E>,
          E : Evaluation<Pr>
{

            cnt_null: 0,
            start_time: Instant::now(),
            master: match BoxedMasterProblem::<MinimalMaster>::new() {
                Ok(master) => Box::new(master),
                Err(err) => return Err(Error::Master(Box::new(err))),
            },
            minorants: vec![],
            iterinfos: vec![],
        })
    }

    /// A new solver with default parameter.
    pub fn new(problem : P) -> Result<Solver<P,Pr,E>> {
        Solver::new_params(problem, SolverParams::default())
    }

    }

    /// Called to update the problem.
    ///
    /// Calling this function typically triggers the problem to
    /// separate new constraints depending on the current solution.
    fn update_problem(&mut self, term: Step) -> Result<bool> {
        let state = UpdateState {minorants: &self.minorants, step: term, iteration_info: &self.iterinfos};
        let updates = match self.problem.update(&state) {
            Ok(updates) => updates,
            Err(err) => return Err(Error::Update(Box::new(err))),
        };

        let mut newvars = Vec::with_capacity(updates.len());
        for u in updates {

        let new_weight = self.weighter.weight(&current_state!(self, Step::Null), &self.params);
        self.master.set_weight(new_weight);
        self.cnt_null += 1;
        debug!("Null Step");
    }

    /// Perform one bundle iteration.
    pub fn step(&mut self) -> Result<Step> {
        self.iterinfos.clear();

        if !self.cur_valid {
            // current point needs new evaluation
            try!(self.init_master());
        }

        try!(self.solve_model());
        if self.terminator.terminate(&current_state!(self, Step::Term), &self.params) {

                primal: Some(nxt_primal),
            });
        }

        if self.new_cutval > self.cur_val + 1e-3 {
            warn!("New minorant has higher value in center new:{} old:{}", self.new_cutval, self.cur_val);
            self.cur_val = self.new_cutval;
            self.iterinfos.push(IterationInfo::NewMinorantTooHigh{new: self.new_cutval, old: self.cur_val});
        }

        self.nxt_val = nxt_ub;

        // check for potential problems with relative precision of all kinds
        if nxt_lb <= descent_bnd {
            // lower bound gives descent step
            if nxt_ub > descent_bnd {
                // upper bound will produce null-step
                if self.cur_val - nxt_lb > (self.cur_val - self.nxt_mod) * self.params.nullstep_factor.max(0.5) {
                    warn!("Relative precision of returned objective interval enforces null-step.");
                    self.iterinfos.push(IterationInfo::UpperBoundNullStep);
                }
            }
        } else {
            // lower bound gives already a null step
            if self.cur_val - nxt_lb > 0.8 * (self.cur_val - self.nxt_mod) {
                // subgradient won't yield much improvement
                warn!("Shallow cut (subgradient won't yield much improvement)");
                self.iterinfos.push(IterationInfo::ShallowCut);
            }
        }

        debug!("Step");
        debug!("  cur_val    ={}", self.cur_val);
        debug!("  nxt_mod    ={}", self.nxt_mod);
        debug!("  nxt_ub     ={}", self.nxt_val);