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// Copyright (c) 2016, 2017, 2018 Frank Fischer <frank-fischer@shadow-soft.de>
//
// This program is free software: you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful, but
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// Copyright (c) 2016, 2017, 2018, 2019 Frank Fischer <frank-fischer@shadow-soft.de>
//
// This program is free software: you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful, but
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Descent,
/// No step but the algorithm has been terminated.
Term,
}
/// Information about a minorant.
#[derive(Debug, Clone)]
struct MinorantInfo<Pr> {
/// The minorant's index in the master problem
index: usize,
/// Current multiplier.
multiplier: Real,
/// Primal associated with this minorant.
primal: Option<Pr>,
}
/// Information about the last iteration.
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum IterationInfo {
NewMinorantTooHigh { new: Real, old: Real },
UpperBoundNullStep,
ShallowCut,
}
/// State information for the update callback.
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()
}
/// Return the last primal for a given subproblem.
///
/// This is the last primal generated by the oracle.
pub fn last_primal(&self, fidx: usize) -> Option<&Pr> {
self.minorants[fidx].last().and_then(|m| m.primal.as_ref())
}
}
/**
* Implementation of a bundle method.
*/
pub struct Solver<P: FirstOrderProblem> {
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Descent,
/// No step but the algorithm has been terminated.
Term,
}
/// Information about a minorant.
#[derive(Debug, Clone)]
struct MinorantInfo {
/// The minorant's index in the master problem
index: usize,
/// Current multiplier.
multiplier: Real,
}
/// Information about the last iteration.
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum IterationInfo {
NewMinorantTooHigh { new: Real, old: Real },
UpperBoundNullStep,
ShallowCut,
}
/// State information for the update callback.
pub struct UpdateState<'a, Pr: 'a> {
/// Current model minorants.
minorants: &'a [Vec<MinorantInfo>],
/// The primals.
primals: &'a Vec<Option<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, self.primals[m.index].as_ref().unwrap()))
.collect()
}
/// Return the last primal for a given subproblem.
///
/// This is the last primal generated by the oracle.
pub fn last_primal(&self, fidx: usize) -> Option<&Pr> {
self.minorants[fidx].last().and_then(|m| self.primals[m.index].as_ref())
}
}
/**
* Implementation of a bundle method.
*/
pub struct Solver<P: FirstOrderProblem> {
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*/
start_time: Instant,
/// The master problem.
master: Box<MasterProblem<MinorantIndex = usize>>,
/// The active minorant indices for each subproblem.
minorants: Vec<Vec<MinorantInfo<P::Primal>>>,
/// Accumulated information about the last iteration.
iterinfos: Vec<IterationInfo>,
}
impl<P: FirstOrderProblem> Solver<P>
where
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*/
start_time: Instant,
/// The master problem.
master: Box<MasterProblem<MinorantIndex = usize>>,
/// The active minorant indices for each subproblem.
minorants: Vec<Vec<MinorantInfo>>,
/// The primals associated with each global minorant index.
primals: Vec<Option<P::Primal>>,
/// Accumulated information about the last iteration.
iterinfos: Vec<IterationInfo>,
}
impl<P: FirstOrderProblem> Solver<P>
where
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sgnorm: 0.0,
expected_progress: 0.0,
cnt_descent: 0,
cnt_null: 0,
start_time: Instant::now(),
master: Box::new(BoxedMasterProblem::new(MinimalMaster::new()?)),
minorants: vec![],
iterinfos: vec![],
})
}
/// A new solver with default parameter.
pub fn new(problem: P) -> Result<Solver<P>, SolverError<P::Err>> {
Solver::new_params(problem, SolverParams::default())
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sgnorm: 0.0,
expected_progress: 0.0,
cnt_descent: 0,
cnt_null: 0,
start_time: Instant::now(),
master: Box::new(BoxedMasterProblem::new(MinimalMaster::new()?)),
minorants: vec![],
primals: vec![],
iterinfos: vec![],
})
}
/// A new solver with default parameter.
pub fn new(problem: P) -> Result<Solver<P>, SolverError<P::Err>> {
Solver::new_params(problem, SolverParams::default())
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///
/// 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<P::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_*`
// fields
cur_y: if term == Step::Descent {
&self.nxt_y
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///
/// 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<P::Err>> {
let updates = {
let state = UpdateState {
minorants: &self.minorants,
primals: &self.primals,
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
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newvars.push((Some(index), lower - value, upper - value, value));
}
}
}
if !newvars.is_empty() {
let 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 |fidx, minidx, vars| {
problem
.extend_subgradient(minorants[fidx][minidx].primal.as_ref().unwrap(), vars)
.map(DVector)
.map_err(|e| e.into())
},
)?;
// modify moved variables
for (index, val) in newvars.iter().filter_map(|v| v.0.map(|i| (i, v.3))) {
self.cur_y[index] = val;
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newvars.push((Some(index), lower - value, upper - value, value));
}
}
}
if !newvars.is_empty() {
let problem = &mut self.problem;
let primals = &self.primals;
self.master.add_vars(
&newvars.iter().map(|v| (v.0, v.1, v.2)).collect::<Vec<_>>(),
&mut |_fidx, minidx, vars| {
problem
.extend_subgradient(primals[minidx].as_ref().unwrap(), vars)
.map(DVector)
.map_err(|e| e.into())
},
)?;
// modify moved variables
for (index, val) in newvars.iter().filter_map(|v| v.0.map(|i| (i, v.3))) {
self.cur_y[index] = val;
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/// 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, &P::Primal)> {
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} \
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/// 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, &P::Primal)> {
self.minorants[subproblem]
.iter()
.map(|m| (m.multiplier, self.primals[m.index].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} \
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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)?,
multiplier: 0.0,
primal: Some(primal),
});
} else {
return Err(SolverError::NoMinorant);
}
}
self.cur_valid = true;
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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];
let minidx = self.master.add_minorant(i, minorant)?;
self.minorants[i].push(MinorantInfo {
index: minidx,
multiplier: 0.0,
});
if minidx >= self.primals.len() {
self.primals.resize_with(minidx + 1, || None);
}
self.primals[minidx] = Some(primal);
} else {
return Err(SolverError::NoMinorant);
}
}
self.cur_valid = true;
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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();
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)?;
// 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>> {
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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();
let (aggr_mins, aggr_primals): (Vec<_>, Vec<_>) = aggr
.into_iter()
.map(|m| (m.index, self.primals[m.index].take().unwrap()))
.unzip();
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,
});
self.primals[aggr_min] = 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>> {
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nxt_lb += fun_lb;
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)?,
multiplier: 0.0,
primal: Some(nxt_primal),
});
}
if self.new_cutval > self.cur_val + 1e-3 {
warn!(
"New minorant has higher value in center new:{} old:{}",
self.new_cutval, self.cur_val
);
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nxt_lb += fun_lb;
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;
let minidx = self.master.add_minorant(fidx, nxt_minorant)?;
self.minorants[fidx].push(MinorantInfo {
index: minidx,
multiplier: 0.0,
});
if minidx >= self.primals.len() {
self.primals.resize_with(minidx + 1, || None);
}
self.primals[minidx] = Some(nxt_primal);
}
if self.new_cutval > self.cur_val + 1e-3 {
warn!(
"New minorant has higher value in center new:{} old:{}",
self.new_cutval, self.cur_val
);
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