RsBundle  Check-in [1318918860]

        }).unwrap();
        solver.terminator = Box::new(StandardTerminator{
            termination_precision: 1e-6
        });
        solver.solve().unwrap();

        let costs : f64 = (0..solver.problem().num_subproblems()).map(|i| {
            let (coeffs, primals) : (Vec<_>, Vec<_>) = solver.aggregated_primals(i).into_iter().unzip();
            let aggr_primals = solver.problem().aggregate_primals_ref(&coeffs, &primals);
            solver.problem().get_primal_costs(i, &aggr_primals)
        }).sum();
        info!("Primal costs: {}", costs);
    } else {
        panic!("Usage: {} FILENAME", program);
    }
}

                (Minorant {
                    constant: objective,
                    linear: g,
                },())
            ],
        })
    }





}

fn main() {
    env_logger::init().unwrap();

    let f = QuadraticProblem::new();
    let mut solver = Solver::new_params(f, SolverParams {

    ///
    /// The function must return the new aggregated primal.
    ///
    /// The default implementation does nothing and simply returns the
    /// last primal. This should work if the implementing problem does
    /// not provide primal information, e.g. if `Self::Primal = ()`.
    #[allow(unused_variables)]
    fn aggregate_primals(&mut self, coeffs: &[Real], primals: Vec<Self::Primal>) -> Self::Primal {
        let mut primals = primals;
        primals.pop().unwrap()
    }
}

                for (i, x) in p.iter().enumerate() {
                    sum += x * self.cbase[fidx][i];
                }
            }
            sum
        }
    }

    /// Aggregate primal vectors.
    pub fn aggregate_primals_ref(&self, coeffs: &[Real], primals: &[&Vec<DVector>]) -> Vec<DVector> {
        let mut aggr = primals[0].iter().map(|x| {
            let mut r = dvec![];
            r.scal(coeffs[0], x);
            r
        }).collect::<Vec<_>>();

        for i in 1..primals.len() {
            for (j,x) in primals[i].iter().enumerate() {
                aggr[j].add_scaled(coeffs[i], x);
            }
        }

        aggr
    }
}

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

    type Primal = Vec<DVector>;

            Ok(SimpleEvaluation {
                objective: objective,
                minorants: vec![(Minorant { constant: objective, linear: subg }, sols)],
            })
        }
    }

    fn aggregate_primals(&mut self, coeffs: &[Real], primals: Vec<Vec<DVector>>) -> Vec<DVector> {




        self.aggregate_primals_ref(coeffs, &primals.iter().map(|x| x).collect::<Vec<_>>())
    }



}

    /// Return the current aggregated primal information for a subproblem.
    ///
    /// This function returns all currently used minorants $x_i$ along
    /// with their coefficients $\alpha_i$. The aggregated primal can
    /// be computed by combining the minorants $\bar{x} =
    /// \sum_{i=1}\^m \alpha_i x_i$.
    pub fn aggregated_primals(&self, subproblem : usize) -> Vec<(Real, &Pr)> {
        self.minorants[subproblem].iter().map(|m| {
            (m.multiplier, m.primal.as_ref().unwrap())
        }).collect()

    }

    fn show_info(&self, step: Step) {
        let time = self.start_time.elapsed();
        info!("{} {:0>2}:{:0>2}:{:0>2}.{:0>2} {:4} {:4} {:4}{:1}  {:9.4} {:9.4} {:12.6e}({:12.6e}) {:12.6e}",
              if step == Step::Term { "_endit" } else { "endit " },
              time.as_secs() / 3600,

            }
            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();
                let (aggr_mins, aggr_primals) : (Vec<_>, Vec<_>) = aggr.into_iter().map(|m| {
                    (m.index, m.primal.unwrap())
                }).unzip();
                let (aggr_min, aggr_coeffs) = try!(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, aggr_primals)),
                });
            }
        }
        Ok(())
    }

    /// Perform a descent step.