RsBundle  Diff

}

impl<E> From<ParameterError> for SolverError<E> {
    fn from(err: ParameterError) -> SolverError<E> {
        SolverError::Parameter(err)
    }
}

impl<E> From<MasterProblemError> for SolverError<E> {
    fn from(err: MasterProblemError) -> SolverError<E> {
        SolverError::Master(err)
    }
}

/**
 * The current state of the bundle method.
 *
 * Captures the current state of the bundle method during the run of
 * the algorithm. This state is passed to certain callbacks like
 * Terminator or Weighter so that they can compute their result

            nxt_mods: dvec![],
            new_cutval: 0.0,
            sgnorm: 0.0,
            expected_progress: 0.0,
            cnt_descent: 0,
            cnt_null: 0,
            start_time: Instant::now(),
            master: Box::new(BoxedMasterProblem::new(
            master: Box::new(BoxedMasterProblem::new(MinimalMaster::new()?)),
                MinimalMaster::new().map_err(SolverError::Master)?,
            )),
            minorants: vec![],
            iterinfos: vec![],
        })
    }

    /// A new solver with default parameter.
    pub fn new(problem: P) -> Result<Solver<P>, SolverError<P::Err>> {

                }
            }
        }

        if !newvars.is_empty() {
            let problem = &mut self.problem;
            let minorants = &self.minorants;
            self.master
            self.master.add_vars(
                .add_vars(
                    &newvars.iter().map(|v| (v.0, v.1, v.2)).collect::<Vec<_>>(),
                    &mut |fidx, minidx, vars| {
                        problem
                            .extend_subgradient(minorants[fidx][minidx].primal.as_ref().unwrap(), vars)
                            .map(DVector)
                            .map_err(|e| e.into())
                    },
                )
                &newvars.iter().map(|v| (v.0, v.1, v.2)).collect::<Vec<_>>(),
                &mut |fidx, minidx, vars| {
                    problem
                        .extend_subgradient(minorants[fidx][minidx].primal.as_ref().unwrap(), vars)
                        .map(DVector)
                        .map_err(|e| e.into())
                },
            )?;
                .map_err(SolverError::Master)?;
            // modify moved variables
            for (index, val) in newvars.iter().filter_map(|v| v.0.map(|i| (i, v.3))) {
                self.cur_y[index] = val;
                self.nxt_y[index] = val;
                self.nxt_d[index] = 0.0;
            }
            // add new variables

     * information.
     */
    fn init_master(&mut self) -> Result<(), SolverError<P::Err>> {
        let m = self.problem.num_subproblems();

        self.master = if m == 1 && self.params.max_bundle_size == 2 {
            debug!("Use minimal master problem");
            Box::new(BoxedMasterProblem::new(
            Box::new(BoxedMasterProblem::new(MinimalMaster::new()?))
                MinimalMaster::new().map_err(SolverError::Master)?,
            ))
        } else {
            debug!("Use CPLEX master problem");
            Box::new(BoxedMasterProblem::new(
            Box::new(BoxedMasterProblem::new(CplexMaster::new()?))
                CplexMaster::new().map_err(SolverError::Master)?,
            ))
        };

        let lb = self.problem.lower_bounds().map(DVector);
        let ub = self.problem.upper_bounds().map(DVector);

        if lb
            .as_ref()
            .map(|lb| lb.len() != self.problem.num_variables())
            .unwrap_or(false)
        {
            return Err(SolverError::Dimension);
        }
        if ub
            .as_ref()
            .map(|ub| ub.len() != self.problem.num_variables())
            .unwrap_or(false)
        {
            return Err(SolverError::Dimension);
        }

        self.master.set_num_subproblems(m).map_err(SolverError::Master)?;
        self.master.set_num_subproblems(m)?;
        self.master
            .set_vars(self.problem.num_variables(), lb, ub)
        self.master.set_vars(self.problem.num_variables(), lb, ub)?;
            .map_err(SolverError::Master)?;
        self.master
            .set_max_updates(self.params.max_updates)
        self.master.set_max_updates(self.params.max_updates)?;
            .map_err(SolverError::Master)?;

        self.minorants = (0..m).map(|_| vec![]).collect();

        self.cur_val = 0.0;
        for i in 0..m {
            let result = self
                .problem
                .evaluate(i, &self.cur_y, INFINITY, 0.0)
                .map_err(SolverError::Evaluation)?;
            self.cur_vals[i] = result.objective();
            self.cur_val += self.cur_vals[i];

            let mut minorants = result.into_iter();
            if let Some((minorant, primal)) = minorants.next() {
                self.cur_mods[i] = minorant.constant;
                self.cur_mod += self.cur_mods[i];
                self.minorants[i].push(MinorantInfo {
                    index: self.master.add_minorant(i, minorant).map_err(SolverError::Master)?,
                    index: self.master.add_minorant(i, minorant)?,
                    multiplier: 0.0,
                    primal: Some(primal),
                });
            } else {
                return Err(SolverError::NoMinorant);
            }
        }

        self.cur_valid = true;

        // Solve the master problem once to compute the initial
        // subgradient.
        //
        // We could compute that subgradient directly by
        // adding up the initial minorants, but this would not include
        // the eta terms. However, this is a heuristic anyway because
        // we assume an initial weight of 1.0, which, in general, will
        // *not* be the initial weight for the first iteration.
        self.master.set_weight(1.0).map_err(SolverError::Master)?;
        self.master.solve(self.cur_val).map_err(SolverError::Master)?;
        self.master.set_weight(1.0)?;
        self.master.solve(self.cur_val)?;
        self.sgnorm = self.master.get_dualoptnorm2().sqrt();

        // Compute the real initial weight.
        let state = current_state!(self, Step::Term);
        let new_weight = self.weighter.weight(&state, &self.params);
        self.master.set_weight(new_weight).map_err(SolverError::Master)?;
        self.master.set_weight(new_weight)?;

        debug!("Init master completed");

        Ok(())
    }

    /// Solve the model (i.e. master problem) to compute the next candidate.
    fn solve_model(&mut self) -> Result<(), SolverError<P::Err>> {
        self.master.solve(self.cur_val).map_err(SolverError::Master)?;
        self.master.solve(self.cur_val)?;
        self.nxt_d = self.master.get_primopt();
        self.nxt_y.add(&self.cur_y, &self.nxt_d);
        self.nxt_mod = self.master.get_primoptval();
        self.sgnorm = self.master.get_dualoptnorm2().sqrt();
        self.expected_progress = self.cur_val - self.nxt_mod;

        // update multiplier from master solution

            if n >= self.params.max_bundle_size {
                // aggregate minorants with smallest coefficients
                self.minorants[i].sort_by_key(|m| -((1e6 * m.multiplier) as isize));
                let aggr = self.minorants[i].split_off(self.params.max_bundle_size - 2);
                let aggr_sum = aggr.iter().map(|m| m.multiplier).sum();
                let (aggr_mins, aggr_primals): (Vec<_>, Vec<_>) =
                    aggr.into_iter().map(|m| (m.index, m.primal.unwrap())).unzip();
                let (aggr_min, aggr_coeffs) = self.master.aggregate(i, &aggr_mins).map_err(SolverError::Master)?;
                let (aggr_min, aggr_coeffs) = self.master.aggregate(i, &aggr_mins)?;
                // append aggregated minorant
                self.minorants[i].push(MinorantInfo {
                    index: aggr_min,
                    multiplier: aggr_sum,
                    primal: Some(
                        self.problem
                            .aggregate_primals(aggr_coeffs.into_iter().zip(aggr_primals.into_iter()).collect()),
                    ),
                });
            }
        }
        Ok(())
    }

    /// Perform a descent step.
    fn descent_step(&mut self) -> Result<(), SolverError<P::Err>> {
        let new_weight = self.weighter.weight(&current_state!(self, Step::Descent), &self.params);
        self.master.set_weight(new_weight).map_err(SolverError::Master)?;
        self.master.set_weight(new_weight)?;
        self.cnt_descent += 1;
        swap(&mut self.cur_y, &mut self.nxt_y);
        swap(&mut self.cur_val, &mut self.nxt_val);
        swap(&mut self.cur_mod, &mut self.nxt_mod);
        swap(&mut self.cur_vals, &mut self.nxt_vals);
        swap(&mut self.cur_mods, &mut self.nxt_mods);
        self.master.move_center(1.0, &self.nxt_d);
        debug!("Descent Step");
        debug!("  dir ={}", self.nxt_d);
        debug!("  newy={}", self.cur_y);
        Ok(())
    }

    /// Perform a null step.
    fn null_step(&mut self) -> Result<(), SolverError<P::Err>> {
        let new_weight = self.weighter.weight(&current_state!(self, Step::Null), &self.params);
        self.master.set_weight(new_weight).map_err(SolverError::Master)?;
        self.master.set_weight(new_weight)?;
        self.cnt_null += 1;
        debug!("Null Step");
        Ok(())
    }

    /// Perform one bundle iteration.
    #[cfg_attr(feature = "cargo-clippy", allow(collapsible_if))]

            nxt_ub += fun_ub;
            self.nxt_vals[fidx] = fun_ub;

            // move center of minorant to cur_y
            nxt_minorant.move_center(-1.0, &self.nxt_d);
            self.new_cutval += nxt_minorant.constant;
            self.minorants[fidx].push(MinorantInfo {
                index: self
                    .master
                    .add_minorant(fidx, nxt_minorant)
                index: self.master.add_minorant(fidx, nxt_minorant)?,
                    .map_err(SolverError::Master)?,
                multiplier: 0.0,
                primal: Some(nxt_primal),
            });
        }

        if self.new_cutval > self.cur_val + 1e-3 {
            warn!(