RsBundle  Diff

            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| {
                                     problem.extend_subgradient(minorants[fidx][minidx].primal.as_ref().unwrap(), vars)
                                         .map(DVector)
                                         .unwrap()
                                 })?;
            // 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

        if let Some(ref x) = lb {
            if x.len() != self.problem.num_variables() {
                return Err(SolverError::Dimension.into());
            }
        }

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

        self.minorants = Vec::with_capacity(m);
        for _ in 0..m {
            self.minorants.push(vec![]);
        }

        self.cur_val = 0.0;

        // 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)?;
        try!(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)?;

        debug!("Init master completed");

        Ok(())
    }

                });
            }
        }
        Ok(())
    }

    /// Perform a descent step.
    fn descent_step(&mut self) -> Result<(), Error> {
        let new_weight = self.weighter.weight(&current_state!(self, Step::Descent), &self.params);
        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<(), Error> {
        let new_weight = self.weighter.weight(&current_state!(self, Step::Null), &self.params);
        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))]
    pub fn step(&mut self) -> Result<Step, Error> {
        self.iterinfos.clear();

        debug!("  cur_val    ={}", self.cur_val);
        debug!("  nxt_mod    ={}", self.nxt_mod);
        debug!("  nxt_ub     ={}", self.nxt_val);
        debug!("  descent_bnd={}", descent_bnd);

        // do a descent step or null step
        if nxt_ub <= descent_bnd {
            self.descent_step()?;
            Ok(Step::Descent)
        } else {
            self.null_step()?;
            Ok(Step::Null)
        }
    }

    /**
     * Return the bound on the function value that enforces a
     * nullstep.