RsBundle  Changes On Branch async-simplify

use c_str_macro::c_str;
use cplex_sys as cpx;
use cplex_sys::trycpx;
use either::Either;
use log::{debug, warn};


use std::collections::VecDeque;
use std::f64::NEG_INFINITY;
use std::iter::{once, repeat};
use std::ops::{Deref, DerefMut};
use std::os::raw::{c_char, c_int};
use std::ptr;
use std::sync::Arc;

// Copyright (c) 2016, 2017, 2018, 2019, 2020 Frank Fischer <frank-fischer@shadow-soft.de>
//
// This program is free software: you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful, but

use crate::{DVector, Real};

use c_str_macro::c_str;
use cplex_sys as cpx;
use cplex_sys::trycpx;


use std::ffi::CString;
use std::ptr;
use std::result;

use std::os::raw::{c_char, c_double, c_int};

#[derive(Debug)]

#[cfg(feature = "crossbeam")]
use rs_crossbeam::channel::{unbounded as channel, RecvError};
#[cfg(not(feature = "crossbeam"))]
use std::sync::mpsc::{channel, RecvError};

use float_pretty_print::PrettyPrintFloat;
use log::{debug, info, warn};

use num_traits::{Float, ToPrimitive, Zero};
use std::iter::repeat;
use std::sync::Arc;
use std::time::Instant;
use threadpool::ThreadPool;

use crate::{DVector, Real};

use super::channels::{
    ChannelResultSender, ChannelUpdateSender, ClientReceiver, ClientSender, EvalResult, Message, Update,
};
use super::masterprocess::{self, MasterConfig, MasterProcess, MasterResponse, Response};
use crate::data::Minorant;
use crate::master::{Builder as MasterBuilder, MasterProblem};
use crate::problem::{FirstOrderProblem, UpdateState};
use crate::terminator::{StandardTerminatable, StandardTerminator, Terminator};
use crate::weighter::{HKWeightable, HKWeighter, Weighter};

pub mod guessmodels;
use guessmodels::{Guess, GuessModel, NearestValue};

/// The default iteration limit.
pub const DEFAULT_ITERATION_LIMIT: usize = 10_000;

/// The default solver.
pub type DefaultSolver<P> = Solver<P, StandardTerminator, HKWeighter, crate::master::FullMasterBuilder>;

#[derive(Clone, Copy, Debug)]
struct EvalId {
    /// The index of the subproblem.
    subproblem: usize,
    /// The index of the candidate at which the subproblem is evaluated.
    candidate_index: usize,
}

/// An evaluation point.
#[derive(Clone)]
pub struct Point {
    /// The globally unique index of the evaluation point.
    index: usize,

    /// The evaluation point itself.
    point: Arc<DVector>,
}

impl Point {
    fn distance(&self, p: &Point) -> Real {
        if self.index != p.index {
            let mut d = self.point.as_ref().clone();
            d.add_scaled(-1.0, &p.point);
            d.norm2()
        } else {
            Real::zero()
        }
    }
}

impl Default for Point {
    fn default() -> Point {
        Point {
            index: 0,
            point: Arc::new(dvec![]),
        }
    }
}

/// Parameters for tuning the solver.
#[derive(Debug, Clone)]
pub struct Parameters {
    /// The descent step acceptance factors, must be in (0,1).
    ///
    /// The default value is 0.1.

    Descent,
    /// No step but the algorithm has been terminated.
    Term,
}

pub struct SolverData {
    /// Current center of stability.
    cur_y: Point,

    /// Function value in the current point.
    cur_val: Real,

    /// Step direction (i.e. nxt_y - cur_y).
    nxt_d: Arc<DVector>,

    /// Current candidate.
    nxt_y: Point,

    /// Function value at the current candidate.
    nxt_val: Real,

    /// Model value at the current candidate.
    nxt_mod: Real,

    /// Number of inner model updates.
    cnt_updates: usize,

    /// Number of descent steps.
    cnt_descent: usize,

    /// The number of subproblems with insufficient evaluation data.
    num_insufficient_candidates: usize,

    /// The number of subproblems that have not been evaluated exactly in the center.
    num_inexact_center: usize,

    /// Whether the next step should be a forced descent step.
    force_descent: bool,

    /// Subproblem data.
    subs: Vec<SubData>,







    /// The list of all evaluation points.
    candidates: Vec<Point>,

    /// Whether we need a new update
    need_update: bool,

    /// Whether a problem update is currently in progress.
    update_in_progress: bool,
}

impl SolverData {
    /// Reset solver data to initial values.
    ///
    /// This means that almost everything is set to +infinity so that
    /// a null-step is forced after the first evaluation.
    fn init(&mut self, y: Point) {
        self.cnt_descent = 0;
        self.cur_y = y.clone();
        self.cur_val = Real::infinity();
        self.nxt_y = y;
        self.nxt_val = Real::infinity();
        self.nxt_mod = -Real::infinity();
        self.nxt_submods = vec![-Real::infinity(); self.nxt_submods.len()];
        self.expected_progress = Real::infinity();
        self.error_bound = Real::infinity();
        self.candidates.clear();

        self.sgnorm = Real::infinity();
        self.cur_weight = 1.0;
        self.num_insufficient_candidates = 0;
        self.num_inexact_center = self.nxt_submods.len();
        self.force_descent = false;
    }
}

impl Default for SolverData {
    fn default() -> SolverData {
        SolverData {
            cur_y: Point::default(),
            cur_val: 0.0,
            nxt_val: 0.0,
            nxt_mod: 0.0,
            nxt_submods: vec![],
            expected_progress: 0.0,
            sgnorm: 0.0,
            error_bound: Real::infinity(),
            cur_weight: 1.0,



            num_insufficient_candidates: 0,
            num_inexact_center: 0,
            force_descent: false,
            candidates: vec![],

            max_iter: 0,
            cnt_descent: 0,
            cnt_updates: 0,
            subs: vec![],
            nxt_d: Arc::new(dvec![]),
            nxt_y: Point::default(),
            need_update: true,
            update_in_progress: false,
        }
    }
}

impl StandardTerminatable for SolverData {

impl HKWeightable for SolverData {
    fn current_weight(&self) -> Real {
        self.cur_weight
    }

    fn center(&self) -> &DVector {
        &self.cur_y.point
    }

    fn center_value(&self) -> Real {
        self.cur_val
    }

    fn candidate_value(&self) -> Real {

    }

    fn aggregated_primal(&self, i: usize) -> &Pr {
        &self.primal_aggrs[i]
    }
}


























































































































































/// Model data of a single subproblem.
///
/// This struct does not handle the subproblem model itself. However, it handles
/// the asynchronous precision data, i.e. the guessed Lipschitz-constant and the
/// distance of the evaluation points to the candidate.
///
/// The concrete model used for computing the guessed values in the candidate
/// and the center must be provided by an implementation of `SubProblem`.
struct SubData {
    /// The index associated with this subproblem.
    fidx: usize,
    /// The subproblem.
    sub: Box<dyn GuessModel>,
    /// The current center.
    center: Point,
    /// The current candidate.
    candidate: Point,
    /// The last index at which the evaluation has been started.
    last_eval_index: usize,
    /// Whether a subproblem evaluation is currently running.
    is_running: bool,
    /// Whether the last evaluation has been sufficiently close.
    is_close_enough: bool,
    /// The original guess value and its evaluation distance in the current center.
    center_guess: Guess,
    /// The current guess of the Lipschitz constant.
    l_guess: Real,
}

impl SubData {
    fn new(fidx: usize, sub: Box<dyn GuessModel>, y: &Point) -> SubData {
        SubData {
            fidx,
            sub,
            last_eval_index: 0,
            center: y.clone(),
            candidate: y.clone(),
            is_running: false,
            is_close_enough: false,
            center_guess: Guess::default(),
            l_guess: 0.0,
        }
    }




    /// Set the center of this model.







    ///


    /// If `update_l_guess` is true also update the guess of the Lipschitz constant.



    fn move_center(&mut self, y: &Point, update_l_guess: bool) {













        assert_eq!(y.index, self.candidate.index, "Must move to current candidate");










        // The guess value used in the current (i.e. old) center


        let old_guess = self.center_guess;
        // The cut value now known for the center.






        let old_cutvalue = self.sub.get_lower_bound(&self.center);

        // There has been a previous evaluation, so first update the Lipschitz guess ...

        if update_l_guess && old_guess.dist > Real::zero() {
            debug!(
                "L-guess fidx:{} guess:{} cut:{}",
                self.fidx, old_guess.value, old_cutvalue
            );
            let new_l_guess = if old_guess.dist.is_finite() {
                (old_cutvalue - old_guess.value) / old_guess.dist
            } else {
                Real::zero()
            };

            if new_l_guess > self.l_guess {
                debug!(
                    "New l_guess fidx:{} old-L:{} L:{}",
                    self.fidx, self.l_guess, new_l_guess
                );
                self.l_guess = new_l_guess;
            }
        }

        // Save the new center
        self.center = y.clone();

        // Save guess value of the candidate/new center
        self.center_guess = self.sub.get_guess_value(&self.center);
    }

    /// Set the candidate of this model.
    fn update_candidate(&mut self, y: &Point, accept_factor: Real) {
        self.candidate = y.clone();
        self.update_close_enough(accept_factor);
    }

    /// Add a function value to this model.
    ///
    /// The `accept_factor` is a parameter for possibly accepting
    /// the candidate guess value as "good enough" (if it has been
    /// changed by the new minorant).
    fn add_function_value(&mut self, y: &Point, value: Real, accept_factor: Real) {
        self.sub.add_function_value(y, value);
        self.update_close_enough(accept_factor)
    }

    /// Add a minorant to this model.
    ///
    /// The `accept_factor` is a parameter for possibly accepting
    /// the candidate guess value as "good enough" (if it has been
    /// changed by the new minorant).
    ///
    /// The minorant must be centered at the global 0.
    fn add_minorant(&mut self, y: &Point, m: &Arc<Minorant>, accept_factor: Real) {
        self.sub.add_minorant(y, m);
        self.update_close_enough(accept_factor)
    }

    /// Return the current guess value at the given point.
    fn get_guess_value(&mut self, y: &Point) -> Guess {
        self.sub.get_guess_value(y)
    }

    /// Return the lower bound at the given point.
    fn get_lower_bound(&mut self, y: &Point) -> Real {
        self.sub.get_lower_bound(y)
    }

    fn update_close_enough(&mut self, accept_factor: Real) {
        let g = self.sub.get_guess_value(&self.candidate);
        self.is_close_enough = g.is_exact() || g.dist * self.l_guess <= accept_factor
    }

    /// Return the error estimation in the center.
    ///
    /// This is the difference between the (current) lower bound and the used
    /// guess value.
    fn error_estimate(&mut self) -> Real {
        self.sub.get_lower_bound(&self.center) - self.center_guess.value
    }

    fn center_guess_value(&self) -> Real {
        self.center_guess.value
    }
}

/// Implementation of a parallel bundle method.
pub struct Solver<P, T = StandardTerminator, W = HKWeighter, M = crate::master::FullMasterBuilder>
where
    P: FirstOrderProblem,

    /// The master problem process.
    master_proc: Option<MasterProcess<P, M::MasterProblem>>,

    /// Whether there is currently a master computation running.
    master_running: bool,

    /// Whether the master problem has been changed.
    master_has_changed: bool,

    /// The channel to receive the evaluation results from subproblems.
    client_tx: Option<ClientSender<EvalId, P, M::MasterProblem>>,

    /// The channel to receive the evaluation results from subproblems.
    client_rx: Option<ClientReceiver<EvalId, P, M::MasterProblem>>,

            weighter: Default::default(),
            problem,
            data: SolverData::default(),

            threadpool: ThreadPool::with_name("Parallel bundle solver".to_string(), ncpus),
            master,
            master_proc: None,
            master_has_changed: false,
            master_running: false,

            client_tx: None,
            client_rx: None,

            start_time: Instant::now(),
        }

    /// This function is automatically called by [`Solver::solve`].
    pub fn init(&mut self) -> Result<(), P, M> {
        debug!("Initialize solver");

        let n = self.problem.num_variables();
        let m = self.problem.num_subproblems();

        self.data.init(Point {
            index: 1,
            point: Arc::new(dvec![Real::zero(); n]),
        });

        let (tx, rx) = channel();
        self.client_tx = Some(tx.clone());
        self.client_rx = Some(rx);

        let master_config = MasterConfig {
            num_subproblems: m,

            self.master.build().map_err(Error::BuildMaster)?,
            master_config,
            tx,
            &mut self.threadpool,
        ));

        debug!("Initial problem evaluation");
        // The initial evaluation point.
        self.data.nxt_y = self.data.cur_y.clone();
        // We need an initial evaluation of all oracles for the first center.
        self.data.subs = (0..m)
            .map(|fidx| SubData::new(fidx, Box::new(NearestValue::new()), &self.data.cur_y))
            .collect();













        // This could be done better: the initial evaluation has index 1!
        self.data.candidates.push(self.data.nxt_y.clone());
        self.data.candidates.push(self.data.nxt_y.clone());

        for i in 0..m {




            self.evaluate_subproblem(i)?;
        }

        self.start_time = Instant::now();

        // wait for all subproblem evaluations.
        let mut cnt_remaining = self.problem.num_subproblems();

        let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
        let client_rx = self.client_rx.as_ref().ok_or(Error::NotInitialized)?;




        while cnt_remaining > 0 {
            let msg = client_rx.recv();
            match msg? {
                Message::Eval(m) => match m {
                    EvalResult::ObjectiveValue { index, value } => {
                        assert_eq!(
                            index.candidate_index, self.data.nxt_y.index,
                            "Receive objective value for unexpected candidate"
                        );

                        self.data.subs[index.subproblem].add_function_value(&self.data.nxt_y, value, 0.0);




                    }
                    EvalResult::Minorant {
                        index,
                        mut minorant,
                        primal,
                    } => {
                        assert_eq!(
                            index.candidate_index, self.data.nxt_y.index,
                            "Receive objective value for unexpected candidate"
                        );
                        // Add the minorant to the master problem.

                        // The minorant is centered at the candidate == center, so it does

                        // not need to be moved.
                        master.add_minorant(index.subproblem, minorant.clone(), primal)?;
                        self.master_has_changed = true;
                        // Center the minorant at 0.
                        minorant.move_center(-1.0, &self.data.nxt_y.point);
                        self.data.subs[index.subproblem].add_minorant(&self.data.nxt_y, &Arc::new(minorant), 0.0);
                    }
                    EvalResult::Done { index } => {
                        self.data.subs[index.subproblem].is_running = false;
                        cnt_remaining -= 1;
                    }
                    EvalResult::Error { err, index } => {
                        self.data.subs[index.subproblem].is_running = false;
                        return Err(Error::Evaluation(err));
                    }
                },
                Message::Update(_) => {
                    unreachable!("Receive update response during initialization");
                }
                Message::Master(_) => {
                    unreachable!("Receive master response during initialization");
                }
            }
        }

        // Set the initial values.
        // For the center this is the current lower bound (cut value),
        // for the candidate it is the model candidate value.
        //
        // Note that both are the same ...
        self.data.cur_val = 0.0;
        self.data.nxt_val = 0.0;
        for s in &mut self.data.subs {
            self.data.cur_val += s.get_lower_bound(&self.data.nxt_y);
            self.data.nxt_val += s.get_guess_value(&self.data.nxt_y).value;
        }
        assert!((self.data.cur_val - self.data.nxt_val).abs() < 1e-6);

        // This is the first evaluation. We effectively get
        // the function value at the current center but
        // we do not have a model estimate yet. Hence, we do not know
        // a good guess for the weight.
        self.data.cur_weight = Real::infinity();
        self.data.need_update = true;

        self.update_problem(Step::Descent)?;

        debug!("First Step");
        debug!("  cur_val={}", self.data.cur_val);
        self.data.cnt_descent += 1;

        // compute the initial candidate
        self.update_candidate()?;

        self.show_info(Step::Descent);

        debug!("Initialization complete");

        Ok(())
    }

    /// Reset data for new iterations.
    fn reset_iteration_data(&mut self, max_iter: usize) {
        let num_variables = self.problem.num_variables();

        self.data.max_iter = max_iter;
        self.data.cnt_updates = 0;
        self.data.nxt_d = Arc::new(dvec![0.0; num_variables]);
        self.data.nxt_y = self.data.cur_y.clone();
        let nxt_y = &self.data.nxt_y;
        self.data.nxt_val = self
            .data
            .subs
            .iter_mut()
            .map(|s| s.get_guess_value(&nxt_y).value)
            .sum::<Real>();
        self.data.need_update = true;
        self.data.update_in_progress = false;
    }

    /// Solve the problem with the default maximal iteration limit [`DEFAULT_ITERATION_LIMIT`].
    pub fn solve(&mut self) -> Result<(), P, M> {
        self.solve_with_limit(DEFAULT_ITERATION_LIMIT)

        loop {
            let msg = self.client_rx.as_ref().ok_or(Error::NotInitialized)?.recv()?;
            match msg {
                Message::Eval(m) => {
                    // Receive a evaluation result
                    if self.handle_client_response(m)? {
                        return Ok(true);
                    }
                }
                Message::Update(msg) => {
                    debug!("Receive update response");
                    if self.handle_update_response(msg)? {
                        // The master problem has been changed so we need a new
                        // candidate as well.
                        self.update_candidate()?;
                    }
                }
                Message::Master(mresponse) => {
                    debug!("Receive master response");
                    // Receive result (new candidate) from the master
                    if self.handle_master_response(mresponse)? {
                        return Ok(true);

        }
    }

    /// A new objective value has been computed.
    fn handle_new_objective(&mut self, id: EvalId, value: Real) -> Result<bool, P, M> {
        debug!(
            "Receive objective from subproblem:{} candidate:{} current:{} obj:{}",
            id.subproblem, id.candidate_index, self.data.nxt_y.index, value
        );

        // check if new evaluation is closer to the current candidate
        let sub = &mut self.data.subs[id.subproblem];
        // Whether the subproblem evaluation had been good enough.
        let was_close_enough = sub.is_close_enough;

        if let Some(nxt) = self.data.candidates.get_mut(id.candidate_index) {
            let accept_factor =
                self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
                    / self.problem.num_subproblems().to_f64().unwrap();



            let old_can_val = sub.get_guess_value(&self.data.nxt_y).value;
            let old_cen_val = sub.get_lower_bound(&self.data.cur_y);

            sub.add_function_value(nxt, value, accept_factor);
            let new_can_val = sub.get_guess_value(&self.data.nxt_y).value;
            let new_cen_val = sub.get_lower_bound(&self.data.cur_y);





            self.data.nxt_val += new_can_val - old_can_val;
            self.data.cur_val += new_cen_val - old_cen_val;


            // This is just a safe-guard: if the function has been evaluated at
            // the current candidate, the evaluation *must* be good enough.
            assert!(
                sub.is_close_enough || sub.last_eval_index < self.data.nxt_y.index,
                "Unexpected insufficiency fidx:{} l:{}",
                id.subproblem,
                sub.l_guess,

            );
        } else {
            // unknown candidate -> ignore objective value
            warn!("Ignore unknown candidate index:{}", id.candidate_index);
            return Ok(false);
        }

        // Test if the new candidate is close enough for the asynchronous
        // precision test.
        if !was_close_enough && sub.is_close_enough {

            debug!(
                "Accept result fidx:{} index:{} candidate:{} (remaining insufficient: {})",
                id.subproblem, id.candidate_index, self.data.nxt_y.index, self.data.num_insufficient_candidates
            );
        }







        self.maybe_do_step(false)
    }
















    /// Add a new minorant.
    ///
    /// The minorant is added to the submodel as well as the master problem. The modified submodel
    /// may then lead to a potential null/descent-step.



    ///

    /// Return values



    ///   - `Ok(true)` if the termination criterion has been satisfied,
    ///   - `Ok(false)` if the termination criterion has not been satisfied,

    ///   - `Err(_)` on error.


    fn handle_new_minorant(&mut self, id: EvalId, minorant: Minorant, primal: P::Primal) -> Result<bool, P, M> {


        debug!(
            "Receive minorant subproblem:{} candidate:{} current:{} center:{}",



            id.subproblem, id.candidate_index, self.data.nxt_y.index, self.data.cur_y.index,
        );





        let accept_factor =








            self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress


                / self.problem.num_subproblems().to_f64().unwrap();






        let sub = &mut self.data.subs[id.subproblem];
        let mut minorant = minorant;


        if let Some(point) = self.data.candidates.get_mut(id.candidate_index) {
            // center the minorant at 0
            minorant.move_center(-1.0, &point.point);



            // add minorant to submodel



            let old_can_val = sub.get_guess_value(&self.data.nxt_y).value;
            let old_cen_val = sub.get_lower_bound(&self.data.cur_y);

            sub.add_minorant(&point, &Arc::new(minorant.clone()), accept_factor);
            let new_can_val = sub.get_guess_value(&self.data.nxt_y).value;
            let new_cen_val = sub.get_lower_bound(&self.data.cur_y);






            self.data.nxt_val += new_can_val - old_can_val;
            self.data.cur_val += new_cen_val - old_cen_val;





            // center the minorant at the current center
            minorant.move_center(1.0, &self.data.cur_y.point);




        } else {
            warn!("Ignore unknown candidate index:{}", id.candidate_index);
            return Ok(false);
        }

        // add minorant to master problem
        let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
        master.add_minorant(id.subproblem, minorant, primal)?;
        self.master_has_changed = true;

        self.maybe_do_step(false)
    }

    /// Handle a response `master_res` from the master problem process.
    ///
    /// The master response is the new candidate point. The method updates the
    /// algorithm state with the data of the new candidate (e.g. the model value
    /// `nxt_mod` in the point or the expected progress). Then it tests whether
    /// a termination criterion is satisfied. This is only the case if there is
    /// no pending problem update.
    ///
    /// Finally the master problem starts the evaluation of all subproblems at
    /// the new candidate.
    ///
    /// The new candidate is immediately checked for a potential new test.
    ///
    /// Return values
    ///   - `Ok(true)` if the termination criterion has been satisfied,
    ///   - `Ok(false)` if the termination criterion has not been satisfied,
    ///   - `Err(_)` on error.
    fn handle_master_response(&mut self, master_res: MasterResponse<P, M::MasterProblem>) -> Result<bool, P, M> {
        match master_res {

                self.data.sgnorm = sgnorm;
                self.data.cnt_updates = cnt_updates;
                self.data.nxt_submods.clear();
                self.data.nxt_submods.extend(nxt_submods);

                debug!(
                    "Master Response current_center:{} current_candidate:{} res_center:{} nxt_mod:{}",
                    self.data.cur_y.index, self.data.nxt_y.index, center_index, self.data.nxt_mod
                );
            }
        };

        self.data.expected_progress = self.data.cur_val - self.data.nxt_mod;

        self.master_running = false;
        let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;

        // If this is the very first solution of the model,
        // we use its result as to make a good guess for the initial weight
        // of the proximal term and resolve.
        if self.data.cur_weight.is_infinite() {
            self.data.cur_weight = self.weighter.initial_weight(&self.data);
            master.set_weight(self.data.cur_weight)?;
            self.master_has_changed = true;
            self.update_candidate()?;
            return Ok(false);
        }











        // Compress bundle
        master.compress()?;





        // Check if new variables had been added. In this case, resize cur_y.
        if self.data.nxt_d.len() > self.data.cur_y.point.len() {
            let nnew = self.data.nxt_d.len() - self.data.cur_y.point.len();
            if nnew != self.data.cur_y.point.len() {
                let mut cur_y = self.data.cur_y.point.as_ref().clone();
                cur_y.extend(repeat(0.0).take(nnew));
                self.data.cur_y.point = Arc::new(cur_y);
            }
        }

        // Compute new candidate.
        let mut next_y = dvec![];
        next_y.add(&self.data.cur_y.point, &self.data.nxt_d);
        #[cfg(debug_assertions)]
        {
            if self.data.nxt_y.point.len() == next_y.len() {
                let mut diff = self.data.nxt_y.point.as_ref().clone();
                diff.add_scaled(-1.0, &next_y);
                debug!("Candidates move distance:{}", diff.norm2());
            }
        }
        self.data.nxt_y.point = Arc::new(next_y);
        self.data.nxt_y.index += 1;


        // Add the new candidate to the list of candidates.
        debug_assert_eq!(self.data.nxt_y.index, self.data.candidates.len());
        self.data.candidates.push(self.data.nxt_y.clone());

        // Update the candidate in all submodels and
        // compute first guess value for new candidate.
        let accept_factor =
            self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
                / self.problem.num_subproblems().to_f64().unwrap();







        self.data.nxt_val = Real::zero();







        for sub in self.data.subs.iter_mut() {


            sub.update_candidate(&self.data.nxt_y, accept_factor);
            self.data.nxt_val += sub.get_guess_value(&self.data.nxt_y).value;







        }









        // Start evaluation of all subproblems at the new candidate.
        for i in 0..self.data.subs.len() {
            self.evaluate_subproblem(i)?;
        }

        self.maybe_do_step(true)
    }

    /// Do a descent or null step if precision is sufficient.
    ///
    /// Also checks if the termination criterion is satisfied.
    ///
    /// Return values
    ///   - `Ok(true)` if the termination criterion has been satisfied,
    ///   - `Ok(false)` if the termination criterion has not been satisfied,
    ///   - `Err(_)` on error.
    fn maybe_do_step(&mut self, check_termination: bool) -> Result<bool, P, M> {
        // No step if there is no real new candidate
        if self.data.nxt_y.index == self.data.cur_y.index {
            return Ok(false);
        }

        self.data.num_insufficient_candidates = self.data.subs.iter().filter(|s| !s.is_close_enough).count();

        let nxt_y = &self.data.nxt_y;
        let num_exact = self
            .data
            .subs
            .iter_mut()
            .map(|s| s.get_guess_value(nxt_y).is_exact())
            .filter(|&is_exact| is_exact)
            .count();

        let sum_dist = self
            .data
            .subs
            .iter_mut()
            .map(|s| s.get_guess_value(nxt_y).dist)
            .sum::<Real>();

        debug!(
            "Number of insufficient subproblems: {} num exact: {} sum dist: {}",
            self.data.num_insufficient_candidates, num_exact, sum_dist,
        );

        // test if we should terminate
        if check_termination
            && self.terminator.terminate(&self.data)
            && !self.data.update_in_progress
            && self.data.cnt_descent > 2
            && !self.data.need_update
        {
            if self.data.num_inexact_center.is_zero() {
                self.show_info(Step::Term);
                info!("Termination criterion satisfied");
                return Ok(true);
            }

            // The termination criterion has been satisfied, but the current
            // center evaluations are not exact. We force the current
            // *candidate* evaluations to be exact and do a forced descent step.
            // This causes the next center to be exact and hopefully satisfying
            // the termination criterion, too.
            let num_inexact = self.data.subs.len() - num_exact;

            debug!(
                "Termination criterion satisfied with {} inexact evaluations",
                num_inexact
            );

            // Current candidate is exact, force a descent step.
            self.data.force_descent = true;
            if num_inexact.is_zero() {
                return self.do_step();
            }

            // Otherwise just wait for the exact evaluations.
            return Ok(false);
        }

        if check_termination {
            self.data.force_descent = false;
        }

        if self.data.num_insufficient_candidates == 0 {
            self.do_step()
        } else {
            Ok(false)
        }
    }

    /// Do a null-step or descent-step based on the current candidate data.
    fn do_step(&mut self) -> Result<bool, P, M> {
        let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
        let descent_bnd = Self::get_descent_bound(self.params.acceptance_factor, &self.data);

        debug!(
            "Try step from center:{} to candidate:{}",
            self.data.cur_y.index, self.data.nxt_y.index
        );

        // Test whether we do a descent step
        if self.data.nxt_val <= descent_bnd || self.data.force_descent {
            debug!("Descent Step{}", if self.data.force_descent { " (forced)" } else { "" });
            debug!("  cur_val    ={}", self.data.cur_val);
            debug!("  nxt_mod    ={}", self.data.nxt_mod);
            debug!("  nxt_ub     ={}", self.data.nxt_val);
            debug!("  descent_bnd={}", descent_bnd);

            self.data.force_descent = false;
            self.data.cnt_descent += 1;
            self.data.cur_y = self.data.nxt_y.clone();
            let cur_y = &self.data.cur_y;
            self.data.num_inexact_center = self
                .data
                .subs
                .iter_mut()
                .map(|s| !s.get_guess_value(&cur_y).is_exact())
                .filter(|&is_exact| is_exact)
                .count();

            // Note that we must update the weight *before* we
            // change the internal data, so the old information
            // that caused the descent step is still available.
            self.data.cur_weight = self.weighter.descent_weight(&self.data);
            // The new value in the center is the model value in the candidate.
            // In particular, it is a lower bound on the real function value.
            //
            // Note that we do not use the model value `nxt_mod`, but the
            // sum of the single model values, because the latter might be higher
            // in case of an aggregated model.
            self.data.cur_val = self.data.nxt_submods.iter().sum();

            // Check if the progress of the last decent step was large enough
            // when using the lower bound in the center instead of the former
            // guess value.
            let error = self.data.subs.iter_mut().map(SubData::error_estimate).sum::<Real>();
            let update_l_guess = error > self.data.error_bound;

            // save new error bound
            self.data.error_bound = self.data.expected_progress * self.params.acceptance_factor;

            // Move all subproblems.
            self.data.nxt_val = Real::zero();
            for sub in &mut self.data.subs {
                sub.move_center(&self.data.cur_y, update_l_guess);
                self.data.nxt_val += sub.get_guess_value(&self.data.nxt_y).value;
            }
            self.data.need_update = true;

            master.move_center(1.0, self.data.nxt_d.clone(), self.data.cur_y.index)?;
            master.set_weight(self.data.cur_weight)?;
            self.master_has_changed = true;

            self.show_info(Step::Descent);
            self.update_problem(Step::Descent)?;

            // We need a new candidate.
            self.update_candidate()?;
            Ok(self.data.cnt_descent >= self.data.max_iter)
        } else {
            debug!("Null Step nxt_val:{} descent_bnd:{}", self.data.nxt_val, descent_bnd);
            // No descent-step, so this is declared a null step
            self.data.cur_weight = self.weighter.null_weight(&self.data);
            self.show_info(Step::Null);
            self.update_problem(Step::Null)?;

            // After a null step we need a new candidate, too.
            self.update_candidate()?;
            Ok(false)
        }
    }

    /// Start evaluation of a subproblem if it is not running.
    ///
    /// The evaluation is started at the current candidate. The candidate
    /// is added to the subproblem's candidate list.
    ///
    /// Returns `true` iff a new evaluations has been started.
    fn evaluate_subproblem(&mut self, subproblem: usize) -> Result<bool, P, M> {
        let sub = &mut self.data.subs[subproblem];
        if !sub.is_running && sub.last_eval_index < self.data.nxt_y.index {
            sub.is_running = true;
            sub.last_eval_index = sub.last_eval_index.max(self.data.nxt_y.index);
            self.problem
                .evaluate(
                    subproblem,
                    self.data.nxt_y.point.clone(),
                    ChannelResultSender::new(
                        EvalId {
                            subproblem,
                            candidate_index: self.data.nxt_y.index,
                        },
                        self.client_tx.as_ref().ok_or(Error::NotInitialized)?.clone(),
                    ),
                )
                .map_err(Error::Evaluation)?;
            debug!("Evaluate fidx:{} candidate:{}", subproblem, self.data.nxt_y.index);
            Ok(true)
        } else {
            assert!(sub.is_running || sub.is_close_enough);
            Ok(false)
        }
    }

    /// Possibly start a new master process computation.
    fn update_candidate(&mut self) -> Result<(), P, M> {

        if !self.master_running && self.master_has_changed {
            debug!("Start master problem");
            self.master_running = true;
            self.master_has_changed = false;
            self.master_proc
                .as_mut()
                .ok_or(Error::NotInitialized)?
                .solve(self.data.cur_val)?;
        }
        Ok(())
    }

        // only one update may be running at the same time
        if self.data.update_in_progress {
            return Ok(false);
        }

        // Ok, we are doing a new update now ...
        self.data.need_update = false;

        // TODO: fix this
        return Ok(false);

        let master_proc = self.master_proc.as_mut().unwrap();
        self.problem
            .update(
                UpdateData {
                    cur_y: self.data.cur_y.point.clone(),
                    nxt_y: self.data.nxt_y.point.clone(),
                    step,
                    primal_aggrs: (0..self.problem.num_subproblems())
                        .map(|i| {
                            master_proc
                                .get_aggregated_primal(i)
                                .map_err(|_| "get_aggregated_primal".to_string())
                                .expect("Cannot get aggregated primal from master process")
                        })
                        .collect(),
                },
                ChannelUpdateSender::new(
                    EvalId {
                        subproblem: 0,
                        candidate_index: self.data.cur_y.index,
                    },
                    self.client_tx.clone().ok_or(Error::NotInitialized)?,
                ),
            )
            .map_err(Error::Update)?;
        self.data.update_in_progress = true;
        Ok(true)

            if step == Step::Descent { "*" } else { " " },
            PrettyPrintFloat(self.data.cur_weight),
            PrettyPrintFloat(self.data.expected_progress()),
            PrettyPrintFloat(self.data.cur_val - self.data.nxt_val),
            PrettyPrintFloat(self.data.nxt_mod),
            PrettyPrintFloat(self.data.nxt_val),
            PrettyPrintFloat(self.data.cur_val),
            PrettyPrintFloat(self.data.subs.iter().map(|s| s.center_guess_value()).sum::<Real>()),
        );
    }

    /// Return the aggregated primal of the given subproblem.
    pub fn aggregated_primal(&self, subproblem: usize) -> Result<P::Primal, P, M> {
        Ok(self
            .master_proc
            .as_ref()
            .ok_or(Error::NotSolved)?
            .get_aggregated_primal(subproblem)?)
    }
}

/*
 * Copyright (c) 2020 Frank Fischer <frank-fischer@shadow-soft.de>
 *
 * This program is free software: you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see  <http://www.gnu.org/licenses/>
 */

use super::Point;
use crate::{data::Minorant, Real};

use num_traits::{Float, Zero};
use std::sync::Arc;

mod nearestvalue;
pub use nearestvalue::NearestValue;

/// A guessed function value.
#[derive(Clone, Copy)]
pub struct Guess {
    /// The guessed value.
    pub value: Real,
    /// The accuracy distance.
    pub dist: Real,
}

impl Guess {
    /// Create a new guess value.
    ///
    /// If `dist` is zero the value is assumed to be exact.
    pub fn new(value: Real, dist: Real) -> Guess {
        Guess { value, dist }
    }

    /// Create an approximate guess value.
    pub fn new_approx(value: Real, dist: Real) -> Guess {
        Guess { value, dist }
    }

    /// Create an exact guess value.
    pub fn new_exact(value: Real) -> Guess {
        Guess {
            value,
            dist: Real::zero(),
        }
    }

    /// Return `true` if this is an exact guess value.
    ///
    /// In other words, the value is not `guessed` anymore.
    pub fn is_exact(&self) -> bool {
        self.dist.is_zero()
    }
}

impl Default for Guess {
    fn default() -> Guess {
        Guess {
            value: Real::zero(),
            dist: Real::infinity(),
        }
    }
}

/// A subproblem model for guessing candidate and center values.
pub trait GuessModel {
    /// Add a function value to this model.
    fn add_function_value(&mut self, y: &Point, value: Real);

    /// Add a minorant to this model.
    ///
    /// The minorant must be centered at the global 0.
    fn add_minorant(&mut self, y: &Point, m: &Arc<Minorant>);

    /// Return a guess value at the given point.
    ///
    /// A guess value is an approximation of the function value in
    /// the given point. If the returned guess value has distance
    /// zero, it must be exact.
    fn get_guess_value(&mut self, y: &Point) -> Guess;

    /// Return a lower bound at the given point.
    fn get_lower_bound(&mut self, y: &Point) -> Real;
}

/*
 * Copyright (c) 2020 Frank Fischer <frank-fischer@shadow-soft.de>
 *
 * This program is free software: you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see  <http://www.gnu.org/licenses/>
 */

//! Asynchronous subproblem with zero-order approximation.

use num_traits::{Float, Zero};
use std::collections::VecDeque;
use std::sync::Arc;

use crate::data::Minorant;
use crate::Real;

use super::{Guess, GuessModel, Point};

/// The maximal number of last evaluation points to be kept in the model.
#[allow(non_upper_case_globals)]
const MaxEvalPoints: usize = 5;
/// The maximal number of last minorants to be kept in the model.
#[allow(non_upper_case_globals)]
const MaxMinorants: usize = 5;

/// Information associated with one subproblem.
pub struct NearestValue {
    /// The last evaluation points (y, function_value).
    eval_points: VecDeque<(Point, Real)>,

    /// The last minorants.
    minorants: VecDeque<Arc<Minorant>>,

    /// The last computed guess value (point, guess).
    last_guess: Option<(Point, Guess)>,

    /// The last computed lower bound (point, value).
    last_lower_bound: Option<(Point, Real)>,
}

impl NearestValue {
    pub fn new() -> NearestValue {
        NearestValue {
            eval_points: VecDeque::new(),
            minorants: VecDeque::new(),
            last_guess: None,
            last_lower_bound: None,
        }
    }
}

impl Default for NearestValue {
    fn default() -> NearestValue {
        NearestValue::new()
    }
}

impl GuessModel for NearestValue {
    fn add_function_value(&mut self, y: &Point, value: Real) {
        // update value at last guess point
        if let Some(ref mut g) = self.last_guess {
            let dist = y.distance(&g.0);
            if dist.is_zero() || dist < g.1.dist {
                g.1 = Guess::new(value, dist);
            }
        }

        // Add evaluation point to list.
        self.eval_points.push_back((y.clone(), value));
        while self.eval_points.len() > MaxEvalPoints {
            self.eval_points.pop_front();
        }
    }

    fn add_minorant(&mut self, _y: &Point, m: &Arc<Minorant>) {
        // update value at last lower-bound point
        if let Some(ref mut lb) = self.last_lower_bound {
            lb.1 = lb.1.max(m.eval(&lb.0.point));
        }

        // Add minorant to list.
        self.minorants.push_back(m.clone());
        while self.minorants.len() > MaxMinorants {
            self.minorants.pop_front();
        }
    }

    fn get_guess_value(&mut self, y: &Point) -> Guess {
        if let Some(ref g) = self.last_guess {
            if g.0.index == y.index {
                return g.1;
            }
        }

        let g = self
            .eval_points
            .iter()
            .map(|(x, value)| Guess::new(*value, x.distance(y)))
            .min_by(|a, b| a.dist.partial_cmp(&b.dist).unwrap())
            .unwrap_or_else(Guess::default);
        self.last_guess = Some((y.clone(), g));
        g
    }

    fn get_lower_bound(&mut self, y: &Point) -> Real {
        // check if y equals the last evaluation point
        if let Some(ref lb) = self.last_lower_bound {
            if lb.0.index == y.index {
                // ... yes, so just return that value
                return lb.1;
            }
        }

        // full computation of lower bound at y
        let lb = self
            .minorants
            .iter()
            .map(|m| m.eval(&y.point))
            .max_by(|a, b| a.partial_cmp(b).unwrap())
            .unwrap_or_else(|| -Real::infinity());
        // save last evaluation
        self.last_lower_bound = Some((y.clone(), lb));
        lb
    }
}

/*
 * Copyright (c) 2019, 2020 Frank Fischer <frank-fischer@shadow-soft.de>
 *
 * This program is free software: you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful, but

#[cfg(feature = "crossbeam")]
use rs_crossbeam::channel::{unbounded as channel, RecvError};
#[cfg(not(feature = "crossbeam"))]
use std::sync::mpsc::{channel, RecvError};

use log::{debug, info, warn};

use num_traits::Float;
use std::sync::Arc;
use std::time::Instant;
use threadpool::ThreadPool;

use crate::{DVector, Real};