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pub struct UpdateState<'a, Pr:'a> {
/// Current model minorants.
minorants: &'a [Vec<MinorantInfo<Pr>>],
/// The last step type.
pub step: Step,
/// Iteration information.
pub iteration_info: &'a [IterationInfo],
/// The current candidate. If the step was a descent step, this is
/// the new center.
pub nxt_y: &'a DVector,
/// The center. IF the step was a descent step, this is the old
/// center.
pub cur_y: &'a DVector,
}
impl<'a, Pr:'a> UpdateState<'a, Pr> {
pub fn aggregated_primals(&self, subproblem : usize) -> Vec<(Real, &Pr)> {
self.minorants[subproblem].iter().map(|m| {
(m.multiplier, m.primal.as_ref().unwrap())
}).collect()
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}
/// 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> {
let updates = {
let state = UpdateState {minorants: &self.minorants, step: term, iteration_info: &self.iterinfos};
let updates = match self.problem.update(&state) {
Ok(updates) => updates,
Err(err) => return Err(Error::Update(Box::new(err))),
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_*`
// fields
cur_y: if term == Step::Descent { &self.nxt_y } else { &self.cur_y },
nxt_y: if term == Step::Descent { &self.cur_y } else { &self.nxt_y },
};
match self.problem.update(&state) {
Ok(updates) => updates,
Err(err) => return Err(Error::Update(Box::new(err))),
}
};
let mut newvars = Vec::with_capacity(updates.len());
for u in updates {
match u {
Update::AddVariable{lower, upper} => {
newvars.push((lower, upper));
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