RsBundle  Check-in [ade34c179c]

 * 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 std::error::Error;

#[macro_use]
extern crate bundle;
extern crate env_logger;

#[macro_use]
extern crate log;

use bundle::{DVector, FirstOrderProblem, Minorant, Real, SimpleEvaluation, Solver, SolverParams};


struct QuadraticProblem {
    a: [[Real; 2]; 2],
    b: [Real; 2],
    c: Real,
}

impl QuadraticProblem {
    fn new() -> QuadraticProblem {
        QuadraticProblem {
            a: [[5.0, 1.0], [1.0, 4.0]],
            b: [-12.0, -10.0],
            c: 3.0,
        }
    }
}

impl<'a> FirstOrderProblem<'a> for QuadraticProblem {
    type Err = Box<dyn Error>;
    type Primal = ();
    type EvalResult = SimpleEvaluation<()>;

    fn num_variables(&self) -> usize {
        2
    }

    #[allow(unused_variables)]
    fn evaluate(
        &'a mut self,
        fidx: usize,
        x: &[Real],
        nullstep_bnd: Real,
        relprec: Real,
    ) -> Result<Self::EvalResult, Self::Err> {
        assert_eq!(fidx, 0);
        let mut objective = self.c;
        let mut g = dvec![0.0; 2];

        for i in 0..2 {
            g[i] += (0..2).map(|j| self.a[i][j] * x[j]).sum::<Real>();
            objective += x[i] * (g[i] + self.b[i]);

//
// You should have received a copy of the GNU General Public License
// along with this program.  If not, see  <http://www.gnu.org/licenses/>
//

//! Problem description of a first-order convex optimization problem.


use solver::UpdateState;
use {Minorant, Real};


use std::result::Result;
use std::vec::IntoIter;

/**
 * Trait for results of an evaluation.
 *
 * An evaluation returns the function value at the point of evaluation
 * and one or more subgradients.
 *

}

/**
 * Trait for implementing a first-order problem description.
 *
 */
pub trait FirstOrderProblem<'a> {
    /// Error raised by this oracle.
    type Err;

    /// The primal information associated with a minorant.
    type Primal;

    /// Custom evaluation result value.
    type EvalResult: Evaluation<Self::Primal>;

    /// Return the number of variables.

     */
    fn evaluate(
        &'a mut self,
        i: usize,
        y: &[Real],
        nullstep_bound: Real,
        relprec: Real,
    ) -> Result<Self::EvalResult, Self::Err>;

    /// Aggregate primal information.
    ///
    /// This function is called from the solver when minorants are
    /// aggregated. The problem can use this information to aggregate
    /// the corresponding primal information.
    ///

        let mut primals = primals;
        primals.pop().unwrap().1
    }

    /// Return updates of the problem.
    ///
    /// The default implementation returns no updates.
    fn update(&mut self, _state: &UpdateState<Self::Primal>) -> Result<Vec<Update>, Self::Err> {
        Ok(vec![])
    }

    /// Return new components for a subgradient.
    ///
    /// The components are typically generated by some primal
    /// information. The corresponding primal is passed as a
    /// parameter.
    ///
    /// The default implementation fails because it should never be
    /// called.
    fn extend_subgradient(&mut self, _primal: &Self::Primal, _vars: &[usize]) -> Result<Vec<Real>, Self::Err> {
        unimplemented!()
    }
}

// along with this program.  If not, see  <http://www.gnu.org/licenses/>
//

//! Proximal bundle method implementation.

#[macro_use]
extern crate c_str_macro;



extern crate itertools;

#[macro_use]
extern crate log;

/// Type used for real numbers throughout the library.
pub type Real = f64;

//
// 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 {DVector, Minorant, Real};

use std::error::Error;
use std::result;

/// Error type for master problems.
///
/// For now they can be arbitrary, unspecified errors.
pub type MasterProblemError = Box<dyn Error>;

/// Result type of master problems.
pub type Result<T> = result::Result<T, MasterProblemError>;

pub trait MasterProblem {
    /// Unique index for a minorant.
    type MinorantIndex: Copy + Eq;

    /// Set the number of subproblems.
    fn set_num_subproblems(&mut self, n: usize) -> Result<()>;

    /// Set the lower and upper bounds of the variables.
    fn set_vars(&mut self, nvars: usize, lb: Option<DVector>, ub: Option<DVector>) -> Result<()>;

    /// Return the current number of minorants of subproblem `fidx`.
    fn num_minorants(&self, fidx: usize) -> usize;

    /// Return the current weight of the quadratic term.
    fn weight(&self) -> Real;

    /// Set the weight of the quadratic term, must be > 0.
    fn set_weight(&mut self, weight: Real) -> Result<()>;

    /// Set the maximal number of inner iterations.
    fn set_max_updates(&mut self, max_updates: usize) -> Result<()>;

    /// Return the current number of inner iterations.
    fn cnt_updates(&self) -> usize;

    /// Add or movesome variables with bounds.
    ///
    /// If an index is specified, existing variables are moved,
    /// otherwise new variables are generated.
    fn add_vars(
        &mut self,
        bounds: &[(Option<usize>, Real, Real)],
        extend_subgradient: &mut FnMut(usize, Self::MinorantIndex, &[usize]) -> DVector,
    ) -> Result<()>;

    /// Add a new minorant to the model.
    ///
    /// The function returns a unique (among all minorants of all
    /// subproblems) index of the minorant. This index must remain
    /// valid until the minorant is aggregated.
    fn add_minorant(&mut self, fidx: usize, minorant: Minorant) -> Result<Self::MinorantIndex>;

    /// Solve the master problem.
    fn solve(&mut self, cur_value: Real) -> Result<()>;

    /// Aggregate the given minorants according to the current
    /// solution.
    ///
    /// The (indices of the) minorants to be aggregated get invalid
    /// after this operation. The function returns the index of the
    /// aggregated minorant along with the coefficients of the convex
    /// combination. The index of the new aggregated minorant might or
    /// might not be one of indices of the original minorants.
    ///
    /// # Error The indices of the minorants `mins` must belong to
    /// subproblem `fidx`.
    fn aggregate(&mut self, fidx: usize, mins: &[usize]) -> Result<(Self::MinorantIndex, DVector)>;

    /// Return the (primal) optimal solution $\\|d\^*\\|$.
    fn get_primopt(&self) -> DVector;

    /// Return the value of the linear model in the optimal solution.
    ///
    /// This is the term $\langle g\^*, d\^* \rangle$ where $g^\*$ is

// along with this program.  If not, see  <http://www.gnu.org/licenses/>
//

use master::MasterProblem;
use master::UnconstrainedMasterProblem;
use {DVector, Minorant, Real};

use super::Result;
use itertools::multizip;
use std::f64::{EPSILON, INFINITY, NEG_INFINITY};


/**
 * Turn unconstrained master problem into box-constrained one.
 *
 * This master problem adds box constraints to an unconstrainted
 * master problem implementation. The box constraints are enforced by
 * an additional outer optimization loop.

            max_updates: 100,
            cnt_updates: 0,
            need_new_candidate: true,
            master,
        }
    }

    pub fn set_max_updates(&mut self, max_updates: usize) -> Result<()> {
        assert!(max_updates > 0);
        self.max_updates = max_updates;
        Ok(())
    }

    /**
     * Update box multipliers $\eta$.

        dualopt.iter().zip(self.eta.iter()).map(|(x, y)| x * y).sum()
    }
}

impl<M: UnconstrainedMasterProblem> MasterProblem for BoxedMasterProblem<M> {
    type MinorantIndex = M::MinorantIndex;

    fn set_num_subproblems(&mut self, n: usize) -> Result<()> {
        self.master.set_num_subproblems(n)
    }

    fn set_vars(&mut self, n: usize, lb: Option<DVector>, ub: Option<DVector>) -> Result<()> {
        assert_eq!(lb.as_ref().map(|x| x.len()).unwrap_or(n), n);
        assert_eq!(ub.as_ref().map(|x| x.len()).unwrap_or(n), n);
        self.lb = lb.unwrap_or_else(|| dvec![NEG_INFINITY; n]);
        self.ub = ub.unwrap_or_else(|| dvec![INFINITY; n]);
        Ok(())
    }

    fn num_minorants(&self, fidx: usize) -> usize {
        self.master.num_minorants(fidx)
    }

    fn add_minorant(&mut self, fidx: usize, minorant: Minorant) -> Result<Self::MinorantIndex> {
        self.master.add_minorant(fidx, minorant)
    }

    fn weight(&self) -> Real {
        self.master.weight()
    }

    fn set_weight(&mut self, weight: Real) -> Result<()> {
        self.master.set_weight(weight)
    }

    fn add_vars(
        &mut self,
        bounds: &[(Option<usize>, Real, Real)],
        extend_subgradient: &mut FnMut(usize, Self::MinorantIndex, &[usize]) -> DVector,
    ) -> Result<()> {
        if !bounds.is_empty() {
            for (index, l, u) in bounds.iter().filter_map(|v| v.0.map(|i| (i, v.1, v.2))) {
                self.lb[index] = l;
                self.ub[index] = u;
            }
            self.lb.extend(bounds.iter().filter(|v| v.0.is_none()).map(|x| x.1));
            self.ub.extend(bounds.iter().filter(|v| v.0.is_none()).map(|x| x.2));
            self.eta.resize(self.lb.len(), 0.0);
            self.need_new_candidate = true;
            let nnew = bounds.iter().filter(|v| v.0.is_none()).count();
            let changed = bounds.iter().filter_map(|v| v.0).collect::<Vec<_>>();
            self.master.add_vars(nnew, &changed, extend_subgradient)
        } else {
            Ok(())
        }
    }

    #[cfg_attr(feature = "cargo-clippy", allow(cyclomatic_complexity))]
    fn solve(&mut self, center_value: Real) -> Result<()> {
        debug!("Solve Master");
        debug!("  lb      ={}", self.lb);
        debug!("  ub      ={}", self.ub);

        if self.need_new_candidate {
            self.compute_candidate();
        }

        debug!("  dualopt={}", self.master.dualopt());
        debug!("  etaopt={}", self.eta);
        debug!("  primoptval={}", self.primoptval);

        Ok(())
    }

    fn aggregate(&mut self, fidx: usize, mins: &[usize]) -> Result<(Self::MinorantIndex, DVector)> {
        self.master.aggregate(fidx, mins)
    }

    fn get_primopt(&self) -> DVector {
        self.primopt.clone()
    }

    fn move_center(&mut self, alpha: Real, d: &DVector) {
        self.need_new_candidate = true;
        self.master.move_center(alpha, d);
        self.lb.add_scaled(-alpha, d);
        self.ub.add_scaled(-alpha, d);
    }

    fn set_max_updates(&mut self, max_updates: usize) -> Result<()> {
        BoxedMasterProblem::set_max_updates(self, max_updates)
    }

    fn cnt_updates(&self) -> usize {
        self.cnt_updates
    }
}

#![allow(unused_unsafe)]

use master::UnconstrainedMasterProblem;
use {DVector, Minorant, Real};

use cplex_sys as cpx;

use super::Result;
use std;
use std::error::Error;
use std::f64::{self, NEG_INFINITY};
use std::fmt;
use std::os::raw::{c_char, c_int};
use std::ptr;
use std::result;

/// A solver error.
#[derive(Debug)]
pub enum CplexMasterError {

    NoMinorants,
}

impl fmt::Display for CplexMasterError {
    fn fmt(&self, fmt: &mut fmt::Formatter) -> result::Result<(), fmt::Error> {
        use self::CplexMasterError::*;
        match self {
            NoMinorants => write!(fmt, "No minorants when solving the master problem"),
        }
    }
}

impl Error for CplexMasterError {}

pub struct CplexMaster {
    lp: *mut cpx::Lp,

    /// True if the QP must be updated.
    force_update: bool,

        unsafe { cpx::freeprob(cpx::env(), &mut self.lp) };
    }
}

impl UnconstrainedMasterProblem for CplexMaster {
    type MinorantIndex = usize;

    fn new() -> Result<CplexMaster> {
        Ok(CplexMaster {
            lp: ptr::null_mut(),
            force_update: true,
            freeinds: vec![],
            updateinds: vec![],
            min2index: vec![],
            index2min: vec![],
            qterm: vec![],
            weight: 1.0,
            minorants: vec![],
            opt_mults: vec![],
            opt_minorant: Minorant::default(),
        })
    }

    fn num_subproblems(&self) -> usize {
        self.minorants.len()
    }

    fn set_num_subproblems(&mut self, n: usize) -> Result<()> {
        trycpx!(cpx::setintparam(
            cpx::env(),
            cpx::Param::Qpmethod.to_c(),
            cpx::Alg::Barrier.to_c()
        ));
        trycpx!(cpx::setdblparam(cpx::env(), cpx::Param::Barepcomp.to_c(), 1e-12));

        self.min2index = vec![vec![]; n];
        self.index2min.clear();
        self.freeinds.clear();
        self.minorants = vec![vec![]; n];
        self.opt_mults = vec![dvec![]; n];

        Ok(())
    }

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

    fn set_weight(&mut self, weight: Real) -> Result<()> {
        assert!(weight > 0.0);
        self.weight = weight;
        Ok(())
    }

    fn num_minorants(&self, fidx: usize) -> usize {
        self.minorants[fidx].len()
    }

    fn add_minorant(&mut self, fidx: usize, minorant: Minorant) -> Result<usize> {
        debug!("Add minorant");
        debug!("  fidx={} index={}: {}", fidx, self.minorants[fidx].len(), minorant);

        let min_idx = self.minorants[fidx].len();
        self.minorants[fidx].push(minorant);
        self.opt_mults[fidx].push(0.0);

    }

    fn add_vars(
        &mut self,
        nnew: usize,
        changed: &[usize],
        extend_subgradient: &mut FnMut(usize, Self::MinorantIndex, &[usize]) -> DVector,
    ) -> Result<()> {
        debug_assert!(!self.minorants[0].is_empty());
        let noldvars = self.minorants[0][0].linear.len();
        let nnewvars = noldvars + nnew;

        let mut changedvars = vec![];
        changedvars.extend_from_slice(changed);
        changedvars.extend(noldvars..nnewvars);

        // WORST CASE: DO THIS
        // self.force_update = true;

        Ok(())
    }

    fn solve(&mut self, eta: &DVector, _fbound: Real, _augbound: Real, _relprec: Real) -> Result<()> {
        if self.force_update || !self.updateinds.is_empty() {
            try!(self.init_qp());
        }

        let nvars = unsafe { cpx::getnumcols(cpx::env(), self.lp) as usize };
        if nvars == 0 {
            return Err(CplexMasterError::NoMinorants.into());

                this_val = this_val.max(m.eval(y));
            }
            result += this_val;
        }
        result
    }

    fn aggregate(&mut self, fidx: usize, mins: &[usize]) -> Result<(usize, DVector)> {
        assert!(!mins.is_empty(), "No minorants specified to be aggregated");

        if mins.len() == 1 {
            return Ok((mins[0], dvec![1.0]));
        }

        // scale coefficients

                m.move_center(alpha, d);
            }
        }
    }
}

impl CplexMaster {
    fn init_qp(&mut self) -> Result<()> {
        if !self.lp.is_null() {
            trycpx!(cpx::freeprob(cpx::env(), &mut self.lp));
        }
        trycpx!({
            let mut status = 0;
            self.lp = cpx::createprob(cpx::env(), &mut status, c_str!("mastercp").as_ptr());
            status

// 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 master::UnconstrainedMasterProblem;
use {DVector, Minorant, Real};

use super::Result;
use std::error::Error;
use std::f64::NEG_INFINITY;
use std::fmt;
use std::result;


/// Minimal master problem error.
#[derive(Debug)]
pub enum MinimalMasterError {

    NumSubproblems { nsubs: usize },
    MaxMinorants,
    NoMinorants,
}

impl fmt::Display for MinimalMasterError {
    fn fmt(&self, fmt: &mut fmt::Formatter) -> result::Result<(), fmt::Error> {
        use self::MinimalMasterError::*;
        match self {
            NumSubproblems { nsubs } => write!(fmt, "Too many subproblems (got: {} must be <= 2)", nsubs),
            MaxMinorants => write!(fmt, "The minimal master problem allows at most two minorants"),
            NoMinorants => write!(fmt, "No minorants when solving the master problem"),
        }
    }
}

impl Error for MinimalMasterError {}

/**
 * A minimal master problem with only two minorants.
 *
 * This is the simplest possible master problem for bundle methods. It
 * has only two minorants and only one function model. The advantage
 * is that this model can be solved explicitely and very quickly, but

    /// Optimal aggregated minorant.
    opt_minorant: Minorant,
}

impl UnconstrainedMasterProblem for MinimalMaster {
    type MinorantIndex = usize;

    fn new() -> Result<MinimalMaster> {
        Ok(MinimalMaster {
            weight: 1.0,
            minorants: vec![],
            opt_mult: dvec![],
            opt_minorant: Minorant::default(),
        })
    }

    fn num_subproblems(&self) -> usize {
        1
    }

    fn set_num_subproblems(&mut self, n: usize) -> Result<()> {
        if n != 1 {
            Err(MinimalMasterError::NumSubproblems { nsubs: n }.into())
        } else {
            Ok(())
        }
    }

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

    fn set_weight(&mut self, weight: Real) -> Result<()> {
        assert!(weight > 0.0);
        self.weight = weight;
        Ok(())
    }

    fn num_minorants(&self, fidx: usize) -> usize {
        assert_eq!(fidx, 0);
        self.minorants.len()
    }

    fn add_minorant(&mut self, fidx: usize, minorant: Minorant) -> Result<usize> {
        assert_eq!(fidx, 0);
        if self.minorants.len() >= 2 {
            return Err(MinimalMasterError::MaxMinorants.into());
        }
        self.minorants.push(minorant);
        self.opt_mult.push(0.0);
        Ok(self.minorants.len() - 1)
    }

    fn add_vars(
        &mut self,
        nnew: usize,
        changed: &[usize],
        extend_subgradient: &mut FnMut(usize, Self::MinorantIndex, &[usize]) -> DVector,
    ) -> Result<()> {
        if !self.minorants.is_empty() {
            let noldvars = self.minorants[0].linear.len();
            let mut changedvars = vec![];
            changedvars.extend_from_slice(changed);
            changedvars.extend(noldvars..noldvars + nnew);
            for (i, m) in self.minorants.iter_mut().enumerate() {
                let new_subg = extend_subgradient(0, i, &changedvars);
                for (&j, &g) in changed.iter().zip(new_subg.iter()) {
                    m.linear[j] = g;
                }
                m.linear.extend_from_slice(&new_subg[changed.len()..]);
            }
        }

        Ok(())
    }

    #[allow(unused_variables)]
    fn solve(&mut self, eta: &DVector, fbound: Real, augbound: Real, relprec: Real) -> Result<()> {
        for (i, m) in self.minorants.iter().enumerate() {
            debug!("  {}:min[{},{}] = {}", i, 0, 0, m);
        }

        if self.minorants.len() == 2 {
            let xx = self.minorants[0].linear.dot(&self.minorants[0].linear);
            let yy = self.minorants[1].linear.dot(&self.minorants[1].linear);

        let mut result = NEG_INFINITY;
        for m in &self.minorants {
            result = result.max(m.eval(y));
        }
        result
    }

    fn aggregate(&mut self, fidx: usize, mins: &[usize]) -> Result<(usize, DVector)> {
        assert_eq!(fidx, 0);
        if mins.len() == 2 {
            debug!("Aggregate");
            debug!("  {} * {}", self.opt_mult[0], self.minorants[0]);
            debug!("  {} * {}", self.opt_mult[1], self.minorants[1]);
            self.minorants[0] = self.minorants[0].combine(self.opt_mult[0], self.opt_mult[1], &self.minorants[1]);
            self.minorants.truncate(1);

//! * changing the weight parameter $w$,
//! * modifying $\hat{f}$ by adding or removing linear functions $\ell_i$,
//! * moving the center of the linear functions $\ell_i$ (and the
//!   bounds), i.e. replacing $\hat{f}$ by $d \mapsto \hat{f}(d -
//!   \hat{d})$ for some given $\hat{d} \in \mathbb{R}\^n$.

mod base;
pub use self::base::{MasterProblem, MasterProblemError as Error, Result};

mod boxed;
pub use self::boxed::BoxedMasterProblem;

mod unconstrained;
pub use self::unconstrained::UnconstrainedMasterProblem;

//
// 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 {DVector, Minorant, Real};

use super::Result;


/**
 * Trait for master problems without box constraints.
 *
 * Implementors of this trait are supposed to solve quadratic
 * optimization problems of the form
 *

 *     \sum_{i=1}\^k \alpha_i \ell_i(d) + \frac{u}{2} \\| d \\|\^2. \\]
 */
pub trait UnconstrainedMasterProblem {
    /// Unique index for a minorant.
    type MinorantIndex: Copy + Eq;

    /// Return a new instance of the unconstrained master problem.
    fn new() -> Result<Self>
    where
        Self: Sized;

    /// Return the number of subproblems.
    fn num_subproblems(&self) -> usize;

    /// Set the number of subproblems (different function models.)
    fn set_num_subproblems(&mut self, n: usize) -> Result<()>;

    /// Return the current weight.
    fn weight(&self) -> Real;

    /// Set the weight of the quadratic term, must be > 0.
    fn set_weight(&mut self, weight: Real) -> Result<()>;

    /// Return the number of minorants of subproblem `fidx`.
    fn num_minorants(&self, fidx: usize) -> usize;

    /// Add a new minorant to the model.
    fn add_minorant(&mut self, fidx: usize, minorant: Minorant) -> Result<Self::MinorantIndex>;

    /// Add or move some variables.
    ///
    /// The variables in `changed` have been changed, so the subgradient
    /// information must be updated. Furthermore, `nnew` new variables
    /// are added.
    fn add_vars(
        &mut self,
        nnew: usize,
        changed: &[usize],
        extend_subgradient: &mut FnMut(usize, Self::MinorantIndex, &[usize]) -> DVector,
    ) -> Result<()>;

    /// Solve the master problem.
    fn solve(&mut self, eta: &DVector, fbound: Real, augbound: Real, relprec: Real) -> Result<()>;

    /// Return the current dual optimal solution.
    fn dualopt(&self) -> &DVector;

    /// Return the current dual optimal solution value.
    fn dualopt_cutval(&self) -> Real;

    /// after this operation. The function returns the index of the
    /// aggregated minorant along with the coefficients of the convex
    /// combination. The index of the new aggregated minorant might or
    /// might not be one of indices of the original minorants.
    ///
    /// # Error
    /// The indices of the minorants `mins` must belong to subproblem `fidx`.
    fn aggregate(&mut self, fidx: usize, mins: &[usize]) -> Result<(Self::MinorantIndex, DVector)>;

    /// Move the center of the master problem along $\alpha \cdot d$.
    fn move_center(&mut self, alpha: Real, d: &DVector);
}

// 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 mcf;
use {DVector, FirstOrderProblem, Minorant, Real, SimpleEvaluation};

use std::error::Error;
use std::f64::INFINITY;
use std::fmt;
use std::fs::File;
use std::io::Read;
use std::result;


/// An error in the mmcf file format.

#[derive(Debug)]

pub struct MMCFFormatError {
    msg: String,
}

impl fmt::Display for MMCFFormatError {
    fn fmt(&self, fmt: &mut fmt::Formatter) -> result::Result<(), fmt::Error> {
        write!(fmt, "Format error: {}", self.msg)
    }
}

impl Error for MMCFFormatError {}

/// Result type of the MMCFProblem.
pub type Result<T> = result::Result<T, Box<Error>>;

#[derive(Clone, Copy, Debug)]
struct ArcInfo {
    arc: usize,
    src: usize,
    snk: usize,
}

    rhs: DVector,
    rhsval: Real,
    cbase: Vec<DVector>,
    c: Vec<DVector>,
}

impl MMCFProblem {
    pub fn read_mnetgen(basename: &str) -> Result<MMCFProblem> {
        let mut buffer = String::new();
        {
            let mut f = try!(File::open(&format!("{}.nod", basename)));
            try!(f.read_to_string(&mut buffer));
        }
        let fnod = buffer
            .split_whitespace()
            .map(|x| x.parse::<usize>().unwrap())
            .collect::<Vec<_>>();

        if fnod.len() != 4 {
            return Err(MMCFFormatError {
                msg: format!("Expected 4 numbers in {}.nod, but got {}", basename, fnod.len()),
            }.into());
        }

        let ncom = fnod[0];
        let nnodes = fnod[1];
        let narcs = fnod[2];

        }

        aggr
    }
}

impl<'a> FirstOrderProblem<'a> for MMCFProblem {
    type Err = Box<dyn Error>;

    type Primal = Vec<DVector>;

    type EvalResult = SimpleEvaluation<Vec<DVector>>;

    fn num_variables(&self) -> usize {
        self.lhs.len()
    }

    #[allow(unused_variables)]
    fn evaluate(
        &'a mut self,
        fidx: usize,
        y: &[Real],
        nullstep_bound: Real,
        relprec: Real,
    ) -> Result<Self::EvalResult> {
        // compute costs
        self.rhsval = 0.0;
        for i in 0..self.c.len() {
            self.c[i].clear();
            self.c[i].extend(self.cbase[i].iter());
        }

#![allow(unused_unsafe)]

use {DVector, Real};

use cplex_sys as cpx;

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

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

pub type Result<T> = result::Result<T, Box<dyn Error>>;

pub struct Solver {
    net: *mut cpx::Net,
}

impl Drop for Solver {
    fn drop(&mut self) {
        unsafe {
            cpx::NETfreeprob(cpx::env(), &mut self.net);
        }
    }
}

impl Solver {
    pub fn new(nnodes: usize) -> Result<Solver> {
        let mut status: c_int;
        let mut net = ptr::null_mut();

        unsafe {
            #[cfg_attr(feature = "cargo-clippy", allow(never_loop))]
            loop {
                status = cpx::setlogfilename(cpx::env(), c_str!("mcf.cpxlog").as_ptr(), c_str!("w").as_ptr());

        unsafe { cpx::NETgetnumnodes(cpx::env(), self.net) as usize }
    }

    pub fn num_arcs(&self) -> usize {
        unsafe { cpx::NETgetnumarcs(cpx::env(), self.net) as usize }
    }

    pub fn set_balance(&mut self, node: usize, supply: Real) -> Result<()> {
        let n = node as c_int;
        let s = supply as c_double;
        trycpx!(cpx::NETchgsupply(cpx::env(), self.net, 1, &n, &s as *const c_double));
        Ok(())
    }

    pub fn set_objective(&mut self, obj: &DVector) -> Result<()> {
        let inds = (0..obj.len() as c_int).collect::<Vec<_>>();
        trycpx!(cpx::NETchgobj(
            cpx::env(),
            self.net,
            obj.len() as c_int,
            inds.as_ptr(),
            obj.as_ptr()
        ));
        Ok(())
    }

    pub fn add_arc(&mut self, src: usize, snk: usize, cost: Real, cap: Real) -> Result<()> {
        let f = src as c_int;
        let t = snk as c_int;
        let c = cost as c_double;
        let u = cap as c_double;
        let name = CString::new(format!("x{}#{}_{}", self.num_arcs() + 1, f + 1, t + 1)).unwrap();
        let cname = name.as_ptr();
        trycpx!(cpx::NETaddarcs(
            cpx::env(),
            self.net,
            1,
            &f,
            &t,
            ptr::null(),
            &u,
            &c,
            &cname as *const *const c_char
        ));
        Ok(())
    }

    pub fn solve(&mut self) -> Result<()> {
        trycpx!(cpx::NETprimopt(cpx::env(), self.net));
        Ok(())
    }

    pub fn objective(&self) -> Result<Real> {
        let mut objval: c_double = 0.0;
        trycpx!(cpx::NETgetobjval(cpx::env(), self.net, &mut objval as *mut c_double));
        Ok(objval)
    }

    pub fn get_solution(&self) -> Result<DVector> {
        let mut sol = dvec![0.0; self.num_arcs()];
        let mut stat: c_int = 0;
        let mut objval: c_double = 0.0;
        trycpx!(cpx::NETsolution(
            cpx::env(),
            self.net,
            &mut stat as *mut c_int,
            &mut objval as *mut c_double,
            sol.as_mut_ptr(),
            ptr::null_mut(),
            ptr::null_mut(),
            ptr::null_mut()
        ));
        Ok(sol)
    }

    pub fn writelp(&self, filename: &str) -> Result<()> {
        let fname = CString::new(filename).unwrap();
        trycpx!(cpx::NETwriteprob(cpx::env(), self.net, fname.as_ptr(), ptr::null_mut()));
        Ok(())
    }
}

//

//! The main bundle method solver.

use {DVector, Real};
use {Evaluation, FirstOrderProblem, HKWeighter, Update};

use master::{BoxedMasterProblem, Error as MasterProblemError, MasterProblem, UnconstrainedMasterProblem};
use master::{CplexMaster, MinimalMaster};

use std::error::Error;
use std::f64::{INFINITY, NEG_INFINITY};
use std::fmt;
use std::mem::swap;
use std::result::Result;
use std::time::Instant;



/// A solver error.
#[derive(Debug)]
pub enum SolverError<E> {
    /// An error occured during oracle evaluation.

    Evaluation(E),
    /// An error occured during oracle update.

    Update(E),
    /// An error has been raised by the master problem.

    Master(MasterProblemError),
    /// The oracle did not return a minorant.

    NoMinorant,
    /// The dimension of some data is wrong.

    Dimension,
    /// Some parameter has an invalid value.

    Parameter(ParameterError),
    /// The lower bound of a variable is larger than the upper bound.

    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 },
    /// Iteration limit has been reached.

    IterationLimit { limit: usize },
}

impl<E: fmt::Display> fmt::Display for SolverError<E> {
    fn fmt(&self, fmt: &mut fmt::Formatter) -> Result<(), fmt::Error> {
        use self::SolverError::*;
        match self {
            Evaluation(err) => write!(fmt, "Oracle evaluation failed: {}", err),
            Update(err) => write!(fmt, "Oracle update failed: {}", err),
            Master(err) => write!(fmt, "Master problem failed: {}", err),
            NoMinorant => write!(fmt, "The oracle did not return a minorant"),
            Dimension => write!(fmt, "Dimension of lower bounds does not match number of variables"),
            Parameter(msg) => write!(fmt, "Parameter error: {}", msg),
            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)
            }
            IterationLimit { limit } => write!(fmt, "The iteration limit of {} has been reached.", limit),
        }
    }
}

impl<E: Error> Error for SolverError<E> {
    fn cause(&self) -> Option<&Error> {
        match self {
            SolverError::Evaluation(err) => Some(err),
            SolverError::Update(err) => Some(err),
            SolverError::Master(err) => Some(err.as_ref()),
            _ => None,
        }
    }
}

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

 * Given the current state of the bundle method, this function determines the
 * weight factor of the quadratic term for the next iteration.
 */
pub trait Weighter {
    /// Return the new weight of the quadratic term.
    fn weight(&mut self, state: &BundleState, params: &SolverParams) -> Real;
}

/// An invalid value for some parameter has been passes.
#[derive(Debug)]
pub struct ParameterError(String);

impl fmt::Display for ParameterError {
    fn fmt(&self, fmt: &mut fmt::Formatter) -> Result<(), fmt::Error> {
        write!(fmt, "{}", self.0)
    }
}

impl Error for ParameterError {}

/// Parameters for tuning the solver.
#[derive(Clone, Debug)]
pub struct SolverParams {
    /// Maximal individual bundle size.
    pub max_bundle_size: usize,

     * variables.
     */
    pub max_updates: usize,
}

impl SolverParams {
    /// Verify that all parameters are valid.
    fn check(&self) -> Result<(), ParameterError> {
        if self.max_bundle_size < 2 {
            Err(ParameterError(format!(
                "max_bundle_size must be >= 2 (got: {})",
                self.max_bundle_size
            )))
        } else if self.acceptance_factor <= 0.0 || self.acceptance_factor >= 1.0 {
            Err(ParameterError(format!(
                "acceptance_factor must be in (0,1) (got: {})",
                self.acceptance_factor
            )))
        } else if self.nullstep_factor <= 0.0 || self.nullstep_factor > self.acceptance_factor {
            Err(ParameterError(format!(
                "nullstep_factor must be in (0,acceptance_factor] (got: {}, acceptance_factor:{})",
                self.nullstep_factor, self.acceptance_factor
            )))
        } else if self.min_weight <= 0.0 {
            Err(ParameterError(format!(
                "min_weight must be in > 0 (got: {})",
                self.min_weight
            )))
        } else if self.max_weight < self.min_weight {
            Err(ParameterError(format!(
                "max_weight must be in >= min_weight (got: {}, min_weight: {})",
                self.max_weight, self.min_weight
            )))
        } else if self.max_updates == 0 {
            Err(ParameterError(format!(
                "max_updates must be in > 0 (got: {})",
                self.max_updates
            )))
        } else {
            Ok(())
        }
    }

        self.minorants[fidx].last().and_then(|m| m.primal.as_ref())
    }
}

/**
 * Implementation of a bundle method.
 */
pub struct Solver<P, Pr, E, Err>
where
    P: for<'a> FirstOrderProblem<'a, Primal = Pr, EvalResult = E, Err = Err>,
    E: Evaluation<Pr>,
{
    /// The first order problem description.
    problem: P,

    /// The solver parameter.
    pub params: SolverParams,

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

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

impl<P, Pr, E, Err> Solver<P, Pr, E, Err>
where
    P: for<'a> FirstOrderProblem<'a, Primal = Pr, EvalResult = E, Err = Err>,
    E: Evaluation<Pr>,
{
    /**
     * Create a new solver for the given problem.
     *
     * Note that the solver owns the problem, so you cannot use the
     * same problem description elsewhere as long as it is assigned to
     * the solver. However, it is possible to get a reference to the
     * internally stored problem using `Solver::problem()`.
     */
    pub fn new_params(problem: P, params: SolverParams) -> Result<Solver<P, Pr, E, Err>, SolverError<Err>> {
        Ok(Solver {
            problem,
            params,
            terminator: Box::new(StandardTerminator {
                termination_precision: 1e-3,
            }),
            weighter: Box::new(HKWeighter::new()),

            )),
            minorants: vec![],
            iterinfos: vec![],
        })
    }

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

    /**
     * Set the first order problem description associated with this
     * solver.
     *

    /// Returns a reference to the solver's current problem.
    pub fn problem(&self) -> &P {
        &self.problem
    }

    /// Initialize the solver.
    pub fn init(&mut self) -> Result<(), SolverError<Err>> {
        self.params.check()?;
        if self.cur_y.len() != self.problem.num_variables() {
            self.cur_valid = false;
            self.cur_y.init0(self.problem.num_variables());
        }

        let lb = self.problem.lower_bounds();

        self.start_time = Instant::now();

        Ok(())
    }

    /// Solve the problem.
    pub fn solve(&mut self) -> Result<(), SolverError<Err>> {
        const LIMIT: usize = 10_000;

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

    /// Solve the problem but stop after `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, SolverError<Err>> {
        for _ in 0..niter {
            let mut term = self.step()?;
            let changed = self.update_problem(term)?;
            // do not stop if the problem has been changed
            if changed && term == Step::Term {
                term = Step::Null
            }
            self.show_info(term);
            if term == Step::Term {
                return Ok(true);
            }
        }
        Ok(false)
    }

    /// Called to update the problem.
    ///
    /// Calling this function typically triggers the problem to
    /// separate new constraints depending on the current solution.
    fn update_problem(&mut self, term: Step) -> Result<bool, SolverError<Err>> {
        let updates = {
            let state = UpdateState {
                minorants: &self.minorants,
                step: term,
                iteration_info: &self.iterinfos,
                // this is a dirty trick: when updating the center, we
                // simply swapped the `cur_*` fields with the `nxt_*`

        if !newvars.is_empty() {
            let mut problem = &mut self.problem;
            let minorants = &self.minorants;
            self.master
                .add_vars(
                    &newvars.iter().map(|v| (v.0, v.1, v.2)).collect::<Vec<_>>(),
                    &mut move |fidx, minidx, vars| match problem

                        .extend_subgradient(minorants[fidx][minidx].primal.as_ref().unwrap(), vars)
                        .map(DVector)
                    {
                        Ok(g) => g,
                        Err(_) => unreachable!(),
                    },
                )
                .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;

    /**
     * Initializes the master problem.
     *
     * The oracle is evaluated once at the initial center and the
     * master problem is initialized with the returned subgradient
     * information.
     */
    fn init_master(&mut self) -> Result<(), SolverError<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(
                MinimalMaster::new().map_err(SolverError::Master)?,
            ))

        debug!("Init master completed");

        Ok(())
    }

    /// Solve the model (i.e. master problem) to compute the next candidate.
    fn solve_model(&mut self) -> Result<(), SolverError<Err>> {
        self.master.solve(self.cur_val).map_err(SolverError::Master)?;
        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;

        debug!("  cur_val ={}", self.cur_val);
        debug!("  nxt_mod ={}", self.nxt_mod);
        debug!("  expected={}", self.expected_progress);
        Ok(())
    }

    /// Reduce size of bundle.
    fn compress_bundle(&mut self) -> Result<(), SolverError<Err>> {
        for i in 0..self.problem.num_subproblems() {
            let n = self.master.num_minorants(i);
            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();

                });
            }
        }
        Ok(())
    }

    /// Perform a descent step.
    fn descent_step(&mut self) -> Result<(), SolverError<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.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<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.cnt_null += 1;
        debug!("Null Step");
        Ok(())
    }

    /// Perform one bundle iteration.
    #[cfg_attr(feature = "cargo-clippy", allow(collapsible_if))]
    pub fn step(&mut self) -> Result<Step, SolverError<Err>> {
        self.iterinfos.clear();

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