/*
 * 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
 * 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/>
 */

//! An asynchronous parallel bundle solver.

#[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_cpus;
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>;

/// The minimal bundle solver.
pub type NoBundleSolver<P> = Solver<P, StandardTerminator, HKWeighter, crate::master::MinimalMasterBuilder>;

/// Error raised by the parallel bundle [`Solver`].
#[derive(Debug)]
#[non_exhaustive]
pub enum Error<MErr, PErr> {
    /// An error raised when creating a new master problem solver.
    BuildMaster(MErr),
    /// An error raised by the master problem process.
    Master(MErr),
    /// The iteration limit has been reached.
    IterationLimit { limit: usize },
    /// An error raised by a subproblem evaluation.
    Evaluation(PErr),
    /// An error raised subproblem update.
    Update(PErr),
    /// The dimension of some data is wrong.
    Dimension(String),
    /// Invalid bounds for a variable.
    InvalidBounds { lower: Real, upper: Real },
    /// The value of a variable is outside its bounds.
    ViolatedBounds { lower: Real, upper: Real, value: Real },
    /// The variable index is out of bounds.
    InvalidVariable { index: usize, nvars: usize },
    /// Disconnected channel.
    Disconnected,
    /// An error occurred in a subprocess.
    Process(RecvError),
    /// A method requiring an initialized solver has been called.
    NotInitialized,
    /// The problem has not been solved yet.
    NotSolved,
    /// Missing function value during initialization for a certain subproblem.
    MissingObjective(usize),
    /// Missing minorant during initialization for a certain subproblem.
    MissingMinorant(usize),
}

/// The result type of the solver.
///
/// - `T` is the value type,
/// - `P` is the `FirstOrderProblem` associated with the solver,
/// - `M` is the `MasterBuilder` associated with the solver.
pub type Result<T, P, M> = std::result::Result<
    T,
    Error<<<M as MasterBuilder>::MasterProblem as MasterProblem>::Err, <P as FirstOrderProblem>::Err>,
>;

impl<MErr, PErr> std::fmt::Display for Error<MErr, PErr>
where
    MErr: std::fmt::Display,
    PErr: std::fmt::Display,
{
    fn fmt(&self, fmt: &mut std::fmt::Formatter) -> std::result::Result<(), std::fmt::Error> {
        use Error::*;
        match self {
            BuildMaster(err) => writeln!(fmt, "Cannot create master problem solver: {}", err),
            Master(err) => writeln!(fmt, "Error in master problem: {}", err),
            IterationLimit { limit } => writeln!(fmt, "The iteration limit has been reached: {}", limit),
            Evaluation(err) => writeln!(fmt, "Error in subproblem evaluation: {}", err),
            Update(err) => writeln!(fmt, "Error in subproblem update: {}", err),
            Dimension(what) => writeln!(fmt, "Wrong dimension for {}", what),
            InvalidBounds { lower, upper } => write!(fmt, "Invalid bounds, lower:{}, upper:{}", lower, upper),
            ViolatedBounds { lower, upper, value } => write!(
                fmt,
                "Violated bounds, lower:{}, upper:{}, value:{}",
                lower, upper, value
            ),
            InvalidVariable { index, nvars } => {
                write!(fmt, "Variable index out of bounds, got:{} must be < {}", index, nvars)
            }
            Disconnected => writeln!(fmt, "A channel got disconnected"),
            Process(err) => writeln!(fmt, "Error in subprocess: {}", err),
            NotInitialized => writeln!(fmt, "The solver must be initialized (called Solver::init()?)"),
            NotSolved => writeln!(fmt, "The problem has not been solved yet"),
            MissingObjective(index) => writeln!(
                fmt,
                "Missing objective value during initialization for subproblem: {}",
                index
            ),
            MissingMinorant(index) => writeln!(
                fmt,
                "Missing minorant value during initialization for subproblem: {}",
                index
            ),
        }
    }
}

impl<MErr, PErr> std::error::Error for Error<MErr, PErr>
where
    MErr: std::error::Error + 'static,
    PErr: std::error::Error + 'static,
{
    fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
        use Error::*;
        match self {
            BuildMaster(err) => Some(err),
            Master(err) => Some(err),
            Evaluation(err) => Some(err),
            Process(err) => Some(err),
            _ => None,
        }
    }
}

impl<MErr, PErr> From<masterprocess::Error<MErr, PErr>> for Error<MErr, PErr>
where
    MErr: std::error::Error + 'static,
{
    fn from(err: masterprocess::Error<MErr, PErr>) -> Error<MErr, PErr> {
        use masterprocess::Error::*;
        match err {
            DisconnectedSender => Error::Disconnected,
            DisconnectedReceiver => Error::Disconnected,
            Aggregation(err) => Error::Master(err),
            SubgradientExtension(err) => Error::Update(err),
            Master(err) => Error::Master(err),
        }
    }
}

impl<MErr, PErr> From<RecvError> for Error<MErr, PErr> {
    fn from(err: RecvError) -> Error<MErr, PErr> {
        Error::Process(err)
    }
}

/// Identifier for a subproblem evaluation.
#[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,
}

/// 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.
    acceptance_factor: Real,

    /// The factor for asynchronous imprecision, must be in (0,1).
    ///
    /// The default value is 0.9.
    imprecision_factor: Real,
}

impl Default for Parameters {
    fn default() -> Self {
        Parameters {
            acceptance_factor: 0.1,
            imprecision_factor: 0.9,
        }
    }
}

impl Parameters {
    /// Change the descent step acceptance factor.
    ///
    /// The default value is 0.1.
    pub fn set_acceptance_factor(&mut self, acceptance_factor: Real) {
        assert!(
            acceptance_factor > 0.0 && acceptance_factor < 1.0,
            "Descent step acceptance factors must be in (0,1), got: {}",
            acceptance_factor
        );
        self.acceptance_factor = acceptance_factor;
    }

    /// Change the imprecision acceptance factor.
    ///
    /// The default value is 0.9.
    pub fn set_imprecision_factor(&mut self, imprecision_factor: Real) {
        assert!(
            imprecision_factor > 0.0 && imprecision_factor < 1.0,
            "Imprecision factor must be in (0,1), got: {}",
            imprecision_factor
        );
        self.imprecision_factor = imprecision_factor;
    }
}

/// The step type that has been performed.
#[derive(Clone, Copy, PartialEq, Eq, Debug)]
pub enum Step {
    /// A null step has been performed.
    Null,
    /// A descent step has been performed.
    Descent,
    /// No step but the algorithm has been terminated.
    Term,
}

pub struct SolverData {
    /// Current center of stability.
    cur_y: Arc<DVector>,

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

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

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

    /// Submodel values at the current candidate.
    nxt_submods: Vec<Real>,

    /// The current expected progress.
    ///
    /// This value is actually `cur_val - nxt_val`. We store it separately only
    /// for debugging purposes because after a descent step `cur_val` will be
    /// changed and we could not see the "old" expected progress anymore that
    /// led to the descent step.
    expected_progress: Real,

    /// Norm of current aggregated subgradient.
    sgnorm: Real,

    /// The error bound for the last descent step.
    error_bound: Real,

    /// The currently used master problem weight.
    cur_weight: Real,

    /// Maximal number of iterations.
    max_iter: usize,

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

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

    /// Index of the current center.
    center_index: usize,

    /// Index of the current candidate.
    candidate_index: 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>,

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

    /// Current candidate.
    nxt_y: Arc<DVector>,

    /// 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: DVector) {
        self.cnt_descent = 0;
        self.cur_y = Arc::new(y);
        self.cur_val = Real::infinity();
        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.candidate_index = 0;
        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: Arc::new(dvec![]),
            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,

            center_index: 0,
            candidate_index: 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: Arc::new(dvec![]),
            need_update: true,
            update_in_progress: false,
        }
    }
}

impl StandardTerminatable for SolverData {
    fn center_value(&self) -> Real {
        self.cur_val
    }

    fn expected_progress(&self) -> Real {
        self.expected_progress
    }
}

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

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

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

    fn candidate_value(&self) -> Real {
        self.nxt_val
    }

    fn candidate_model(&self) -> Real {
        self.nxt_mod
    }

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

    fn sgnorm(&self) -> Real {
        self.sgnorm
    }
}

/// Data providing access for updating the problem.
struct UpdateData<Pr> {
    /// Type of step.
    step: Step,

    /// Current center of stability.
    cur_y: Arc<DVector>,

    /// Current candidate.
    nxt_y: Arc<DVector>,

    /// The master process.
    primal_aggrs: Vec<Pr>,
}

impl<Pr> UpdateState<Pr> for UpdateData<Pr>
where
    Pr: Send + 'static,
{
    fn was_descent(&self) -> bool {
        self.step == Step::Descent
    }

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

    fn candidate(&self) -> &DVector {
        &self.nxt_y
    }

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

/// 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()
        }
    }
}

/// 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 candidate index.
    candidate_index: usize,
    /// 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 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>) -> SubData {
        SubData {
            fidx,
            sub,
            last_eval_index: 0,
            candidate_index: 0,
            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 set_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.center_lb();

        // 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 guess value of the candidate/new center
        self.center_guess = self.sub.candidate_value().unwrap_or_else(Guess::default);

        // Update submodel.
        self.sub.set_center(y);
    }

    /// Set the candidate of this model.
    fn set_candidate(&mut self, y: &Point, accept_factor: Real) {
        self.sub.set_candidate(y);
        self.candidate_index = y.index;
        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 current candidate.
    ///
    /// The function might return `None` if no guess value is available.
    fn candidate_value(&self) -> Option<Guess> {
        self.sub.candidate_value()
    }

    /// Return the current lower bound value at the center.
    fn center_lb(&self) -> Real {
        self.sub.center_lb()
    }

    fn update_close_enough(&mut self, accept_factor: Real) {
        self.is_close_enough = self
            .sub
            .candidate_value()
            .map(|v| v.is_exact() || v.dist * self.l_guess <= accept_factor)
            .unwrap_or(false);
    }

    /// Return the error estimation in the center.
    ///
    /// This is the difference between the (current) lower bound and the used
    /// guess value.
    fn error_estimate(&self) -> Real {
        self.sub.center_lb() - 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,
    P::Err: 'static,
    M: MasterBuilder,
{
    /// Parameters for the solver.
    pub params: Parameters,

    /// Termination predicate.
    pub terminator: T,

    /// Weighter heuristic.
    pub weighter: W,

    /// The threadpool of the solver.
    pub threadpool: ThreadPool,

    /// The master problem builder.
    pub master: M,

    /// The first order problem.
    problem: P,

    /// The algorithm data.
    data: SolverData,

    /// 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>>,

    /// Time when the solution process started.
    ///
    /// This is actually the time of the last call to `Solver::init`.
    start_time: Instant,
}

impl<P, T, W, M> Solver<P, T, W, M>
where
    P: FirstOrderProblem,
    P::Err: Send + 'static,
    T: Terminator<SolverData> + Default,
    W: Weighter<SolverData> + Default,
    M: MasterBuilder,
    <M::MasterProblem as MasterProblem>::MinorantIndex: std::hash::Hash,
{
    /// Create a new parallel bundle solver.
    pub fn new(problem: P) -> Self
    where
        M: Default,
    {
        Self::with_master(problem, M::default())
    }

    /// Create a new parallel bundle solver.
    pub fn with_master(problem: P, master: M) -> Self {
        let ncpus = num_cpus::get();
        info!("Initializing asynchronous solver with {} CPUs", ncpus);
        Solver {
            params: Parameters::default(),
            terminator: Default::default(),
            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(),
        }
    }

    /// Return the underlying threadpool.
    ///
    /// In order to use the same threadpool for concurrent processes,
    /// just clone the returned `ThreadPool`.
    pub fn threadpool(&self) -> &ThreadPool {
        &self.threadpool
    }

    /// Set the threadpool.
    ///
    /// This function allows to use a specific threadpool for all processes
    /// spawned by the solver. Note that this does not involve any threads
    /// used by the problem because the solver is not responsible for executing
    /// the evaluation process of the subproblems. However, the problem might
    /// use the same threadpool as the solver.
    pub fn set_threadpool(&mut self, threadpool: ThreadPool) {
        self.threadpool = threadpool;
    }

    /// Return the current problem associated with the solver.
    pub fn problem(&self) -> &P {
        &self.problem
    }

    /// Initialize the solver.
    ///
    /// This will reset the internal data structures so that a new fresh
    /// solution process can be started.
    ///
    /// It will also setup all worker processes.
    ///
    /// 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(dvec![0.0; n]);

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

        let master_config = MasterConfig {
            num_subproblems: m,
            num_vars: n,
            lower_bounds: self.problem.lower_bounds().map(DVector),
            upper_bounds: self.problem.upper_bounds().map(DVector),
        };

        if master_config
            .lower_bounds
            .as_ref()
            .map(|lb| lb.len() != n)
            .unwrap_or(false)
        {
            return Err(Error::Dimension("lower bounds".to_string()));
        }
        if master_config
            .upper_bounds
            .as_ref()
            .map(|ub| ub.len() != n)
            .unwrap_or(false)
        {
            return Err(Error::Dimension("upper bounds".to_string()));
        }

        debug!("Start master process");
        self.master_proc = Some(MasterProcess::start(
            self.master.build().map_err(Error::BuildMaster)?,
            master_config,
            tx,
            &mut self.threadpool,
        ));

        debug!("Initial problem evaluation");
        // 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())))
            .collect();
        self.data.nxt_y = self.data.cur_y.clone();

        // The initial evaluation point.
        let point = Point {
            index: self.data.candidate_index,
            point: self.data.cur_y.clone(),
        };

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

        for i in 0..m {
            self.data.subs[i].set_candidate(&point, 0.0);
            self.data.subs[i].set_center(&point, false);
            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.candidate_index,
                            "Receive objective value for unexpected candidate"
                        );
                        self.data.subs[index.subproblem].add_function_value(&point, value, 0.0);
                    }
                    EvalResult::Minorant {
                        index,
                        mut minorant,
                        primal,
                    } => {
                        assert_eq!(
                            index.candidate_index, self.data.candidate_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, &point.point);
                        self.data.subs[index.subproblem].add_minorant(&point, &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 &self.data.subs {
            self.data.cur_val += s.center_lb();
            self.data.nxt_val += s.candidate_value().unwrap().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 = Arc::new(dvec![]);
        self.data.nxt_val = self
            .data
            .subs
            .iter()
            .map(|s| s.candidate_value().unwrap().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)
    }

    /// Solve the problem with a maximal iteration limit.
    pub fn solve_with_limit(&mut self, limit: usize) -> Result<(), P, M> {
        // First initialize the internal data structures.
        self.init()?;

        if self.solve_iter(limit)? {
            Ok(())
        } else {
            Err(Error::IterationLimit { limit })
        }
    }

    /// Solve the problem but stop after at most `niter` iterations.
    ///
    /// The function returns `Ok(true)` if the termination criterion
    /// has been satisfied. Otherwise it returns `Ok(false)` or an
    /// error code.
    ///
    /// If this function is called again, the solution process is
    /// continued from the previous point. Because of this one *must*
    /// call `init()` before the first call to this function.
    pub fn solve_iter(&mut self, niter: usize) -> Result<bool, P, M> {
        debug!("Start solving up to {} iterations", niter);

        self.reset_iteration_data(niter);

        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);
                    }
                }
            }
        }
    }

    /// Handle a response `msg` from a subproblem evaluation.
    ///
    /// The result from a subproblem evaluation is either a (upper bound on the)
    /// function value is some point or a subgradient (or linear minorant) on
    /// the function in some point.
    ///
    /// The current implementation will wait until all subproblems have created
    /// at least one function value and one subgradient. Once this is true,
    /// either a descent step or a null step is performed depending on the
    /// outcome of the descent test.
    ///
    /// After the step the problem gets a chance to update (e.g. generate new
    /// variables), and the master problem is started to compute a new
    /// candidate.
    ///
    /// The function returns
    ///   - `Ok(true)` if the final iteration count has been reached,
    ///   - `Ok(false)` if the final iteration count has not been reached,
    ///   - `Err(_)` on error.
    fn handle_client_response(&mut self, msg: EvalResult<EvalId, P::Primal, P::Err>) -> Result<bool, P, M> {
        match msg {
            EvalResult::ObjectiveValue { index, value } => self.handle_new_objective(index, value),
            EvalResult::Minorant {
                index,
                minorant,
                primal,
            } => self.handle_new_minorant(index, minorant, primal),
            EvalResult::Done { index } => {
                let sub = &mut self.data.subs[index.subproblem];
                sub.is_running = false;

                // possibly restart the subproblem for the current candidate
                self.evaluate_subproblem(index.subproblem)?;
                Ok(false)
            }
            EvalResult::Error { err, index } => {
                self.data.subs[index.subproblem].is_running = false;
                Err(Error::Evaluation(err))
            }
        }
    }

    /// 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.candidate_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.candidate_value().unwrap().value;
            let old_cen_val = sub.center_lb();
            sub.add_function_value(nxt, value, accept_factor);
            let new_can_val = sub.candidate_value().unwrap().value;
            let new_cen_val = sub.center_lb();

            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.candidate_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.candidate_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.candidate_index, self.data.center_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.candidate_value().unwrap().value;
            let old_cen_val = sub.center_lb();
            sub.add_minorant(&point, &Arc::new(minorant.clone()), accept_factor);
            let new_can_val = sub.candidate_value().unwrap().value;
            let new_cen_val = sub.center_lb();

            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);
        } 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 {
            Response::Error(err) => return Err(err.into()),
            Response::Result {
                nxt_mod,
                sgnorm,
                cnt_updates,
                nxt_d,
                nxt_submods,
                center_index,
            } => {
                self.data.nxt_d = Arc::new(nxt_d);
                self.data.nxt_mod = nxt_mod;
                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.center_index, self.data.candidate_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()?;

        // Compute new candidate.
        let mut next_y = dvec![];
        self.data.candidate_index += 1;

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

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

        // Add the new candidate to the list of candidates.
        let candidate_index = self.data.candidates.len();
        let can = Point {
            index: candidate_index,
            point: self.data.nxt_y.clone(),
        };
        self.data.candidates.push(can.clone());

        // Update the candidate in all submodels.
        let accept_factor =
            self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
                / self.problem.num_subproblems().to_f64().unwrap();

        // Reset evaluation data.
        let old_nxt_val = self.data.nxt_val;
        self.data.nxt_val = 0.0;
        for sub in self.data.subs.iter_mut() {
            sub.set_candidate(&can, accept_factor);
            self.data.nxt_val += sub.candidate_value().unwrap().value;
        }
        debug!("RESET old:{} new:{}", old_nxt_val, self.data.nxt_val);

        // 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.candidate_index == self.data.center_index {
            return Ok(false);
        }

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

        let num_exact = self
            .data
            .subs
            .iter()
            .filter(|s| s.candidate_value().unwrap().is_exact())
            .count();

        let sum_dist = self
            .data
            .subs
            .iter()
            .map(|s| s.candidate_value().unwrap().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.center_index, self.data.candidate_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.center_index = self.data.candidate_index;
            self.data.num_inexact_center = self
                .data
                .subs
                .iter()
                .filter(|s| !s.candidate_value().unwrap().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);
            self.data.cur_y = self.data.nxt_y.clone();
            // 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().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.
            let point = Point {
                index: self.data.center_index,
                point: self.data.cur_y.clone(),
            };
            self.data.nxt_val = Real::zero();
            for sub in &mut self.data.subs {
                sub.set_center(&point, update_l_guess);
                self.data.nxt_val += sub.candidate_value().unwrap().value;
            }
            self.data.need_update = true;

            master.move_center(1.0, self.data.nxt_d.clone(), self.data.center_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.candidate_index {
            sub.is_running = true;
            sub.last_eval_index = sub.last_eval_index.max(self.data.candidate_index);
            self.problem
                .evaluate(
                    subproblem,
                    self.data.nxt_y.clone(),
                    ChannelResultSender::new(
                        EvalId {
                            subproblem,
                            candidate_index: self.data.candidate_index,
                        },
                        self.client_tx.as_ref().ok_or(Error::NotInitialized)?.clone(),
                    ),
                )
                .map_err(Error::Evaluation)?;
            debug!("Evaluate fidx:{} candidate:{}", subproblem, self.data.candidate_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(())
    }

    /// Update the problem after descent or null `step`.
    ///
    /// After a successful descent or null step the problem gets a chance to
    /// update, i.e. to change some problem properties like adding new
    /// variables. This method starts the update process.
    ///
    /// Return values
    ///   - `Ok(true)` if a new update process has been started,
    ///   - `Ok(false)` if there is already a running update process (only one
    ///     is allowed at the same time),
    ///   - `Err(_)` on error.
    fn update_problem(&mut self, step: Step) -> Result<bool, P, M> {
        // 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.clone(),
                    nxt_y: self.data.nxt_y.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.center_index,
                    },
                    self.client_tx.clone().ok_or(Error::NotInitialized)?,
                ),
            )
            .map_err(Error::Update)?;
        self.data.update_in_progress = true;
        Ok(true)
    }

    /// Handles an update response `update` of the problem.
    ///
    /// The method is called if the problem informs the solver about a change of
    /// the problem, e.g. adding a new variable. This method informs the other
    /// parts of the solver, e.g. the master problem, about the modification.
    ///
    /// The method returns `Ok(true)` if the master problem has been modified.
    fn handle_update_response(&mut self, update: Update<EvalId, P::Primal, P::Err>) -> Result<bool, P, M> {
        let mut modified = false;
        match update {
            Update::AddVariables { bounds, sgext, .. } => {
                if !bounds.is_empty() {
                    // add new variables
                    self.master_proc
                        .as_mut()
                        .unwrap()
                        .add_vars(bounds.into_iter().map(|(l, u)| (None, l, u)).collect(), sgext)?;
                    modified = true;
                }
            }
            Update::Done { .. } => self.data.update_in_progress = false, // currently we do nothing ...
            Update::Error { err, .. } => {
                self.data.update_in_progress = false;
                return Err(Error::Update(err));
            }
        }

        Ok(modified)
    }

    /// Return the bound the function value must be below of to enforce a descent step.
    ///
    /// If the oracle guarantees that $f(\bar{y}) \le$ this bound, the
    /// bundle method will perform a descent step.
    ///
    /// This value is $f(\hat{y}) + \varrho \cdot \Delta$ where
    /// $\Delta = f(\hat{y}) - \hat{f}(\bar{y})$ is the expected
    /// progress and $\varrho$ is the `acceptance_factor`.
    fn get_descent_bound(acceptance_factor: Real, data: &SolverData) -> Real {
        data.cur_val - acceptance_factor * (data.cur_val - data.nxt_mod)
    }

    /// Log some information about the latest `step`.
    fn show_info(&self, step: Step) {
        let time = self.start_time.elapsed();
        info!(
            "{} {:0>2}:{:0>2}:{:0>2}.{:0>2} {:4} {:4}{:1}  {:9.4} {:9.4}({:9.4}) \
             {:12.6}({:12.6}) {:12.6}({:12.6})",
            if step == Step::Term { "_endit" } else { "endit " },
            time.as_secs() / 3600,
            (time.as_secs() / 60) % 60,
            time.as_secs() % 60,
            time.subsec_nanos() / 10_000_000,
            self.data.cnt_descent,
            self.data.cnt_updates,
            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)?)
    }
}
