/*
* Copyright (c) 2019-2023 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
* WITHOUT ANY WARRANTY; without even the implied warranty of
* 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/>
*/
//! An asynchronous parallel bundle solver.
#[cfg(feature = "crossbeam")]
use rs_crossbeam::channel::{unbounded as channel, RecvError};
#[cfg(not(feature = "crossbeam"))]
use std::sync::mpsc::{channel, RecvError};
use float_pretty_print::PrettyPrintFloat;
use log::{debug, info, log_enabled, warn, Level::Debug};
use num_traits::{Float, FromPrimitive, One, ToPrimitive, Zero};
use std::cmp::Ordering;
use std::collections::HashMap;
use std::iter::repeat;
use std::sync::Arc;
use std::time::Instant;
use thiserror::Error;
use threadpool::ThreadPool;
use crate::{DVector, Minorant, Real};
use super::channels::{
ChannelResultSender, ChannelUpdateSender, ClientReceiver, ClientSender, EvalResult, Message, Update,
};
use super::masterprocess::{MasterError, MasterProcess, MasterResponse, Response};
use crate::master::{Builder as MasterBuilder, MasterProblem};
use crate::problem::{FirstOrderProblem, SubgradientExtender, UpdateState};
use crate::terminator::{StandardTerminatable, StandardTerminator, Terminator};
use crate::weighter::{HKWeightable, HKWeighter, Weighter};
pub mod guessmodels;
pub use guessmodels::GuessModelType;
use guessmodels::{CutModel, Guess, GuessModel, NearestValue};
/// The default iteration limit.
pub const DEFAULT_ITERATION_LIMIT: usize = 10_000;
/// The default solver.
pub type DefaultSolver<P> = Solver<P, StandardTerminator, HKWeighter, crate::master::FullMasterBuilder>;
/// The minimal bundle solver.
pub type NoBundleSolver<P> = Solver<P, StandardTerminator, HKWeighter, crate::master::MinimalMasterBuilder>;
/// Error raised by the parallel bundle [`Solver`].
#[derive(Debug, Error)]
#[non_exhaustive]
pub enum Error<PErr, MErr> {
#[error("master problem creation failed")]
BuildMaster(#[source] MErr),
#[error("error solving the master problem")]
Master(#[source] MasterError<PErr, MErr>),
#[error("iteration limit ({limit}) has been reached")]
IterationLimit { limit: usize },
#[error("problem evaluation failed")]
Evaluation(PErr),
#[error("problem update failed")]
Update(PErr),
#[error("subgradient extension failed")]
SubgradientExtension(PErr),
#[error("wrong dimension for {what}")]
Dimension { what: String },
#[error("invalid variable bounds (lower: {lower}, upper: {upper})")]
InvalidBounds { lower: Real, upper: Real },
#[error("Lower bound for convexity constraint variable with index {0} must be zero.")]
InvalidConvexityLowerBound(usize),
#[error("Convexity constraint variable with index {0} must be unbounded from above.")]
InvalidConvexityUpperBound(usize),
#[error("disconnected receiving channel to a subprocess")]
DisconnectedReceiver(#[source] RecvError),
#[error("solver has not been initialized")]
NotInitialized,
#[error("problem has not been solved")]
NotSolved,
#[error("missing function value during initialization for subproblem {0}")]
MissingObjective(usize),
#[error("missing minorant during initialization for subproblem {0}")]
MissingMinorant(usize),
}
/// The result type of the solver.
///
/// - `T` is the value type,
/// - `P` is the `FirstOrderProblem` associated with the solver,
/// - `M` is the `MasterBuilder` associated with the solver.
pub type Result<T, P, M> = std::result::Result<
T,
Error<
<P as FirstOrderProblem>::Err,
<<M as MasterBuilder<<P as FirstOrderProblem>::Minorant>>::MasterProblem as MasterProblem<
<P as FirstOrderProblem>::Minorant,
>>::Err,
>,
>;
impl<PErr, MErr> From<MasterError<PErr, MErr>> for Error<PErr, MErr> {
fn from(err: MasterError<PErr, MErr>) -> Error<PErr, MErr> {
match err {
MasterError::SubgradientExtension(e) => Error::SubgradientExtension(e),
_ => Error::Master(err),
}
}
}
impl<PErr, MErr> From<RecvError> for Error<PErr, MErr> {
fn from(err: RecvError) -> Error<PErr, MErr> {
Error::DisconnectedReceiver(err)
}
}
/// Identifier for a subproblem evaluation.
#[derive(Clone, Copy, Debug)]
struct EvalId {
/// The index of the subproblem.
subproblem: usize,
/// The index of the candidate at which the subproblem is evaluated.
candidate_index: usize,
}
/// An evaluation point.
#[derive(Clone)]
pub struct Point {
/// The globally unique index of the evaluation point.
index: usize,
/// The evaluation point itself.
point: Arc<DVector>,
}
impl Point {
fn distance(&self, p: &Point) -> Real {
if self.index != p.index {
let mut d = self.point.as_ref().clone();
d.add_scaled(-1.0, &p.point);
d.norm2()
} else {
Real::zero()
}
}
}
impl Default for Point {
fn default() -> Point {
Point {
index: 0,
point: Arc::new(dvec![]),
}
}
}
/// Parameters for tuning the solver.
#[derive(Debug, Clone)]
pub struct Parameters {
/// The guess model type to be used.
pub guess_model: GuessModelType,
/// The increase-factor of the Lipschitz-constant guess, must be in (0,1].
l_guess_factor: Real,
/// The descent step acceptance factors, must be in (0,1).
///
/// The default value is 0.1.
acceptance_factor: Real,
/// The factor for asynchronous imprecision, must be in (0,1).
///
/// The default value is 0.9.
imprecision_factor: Real,
}
impl Default for Parameters {
fn default() -> Self {
Parameters {
guess_model: GuessModelType::CutModel,
l_guess_factor: 0.1,
acceptance_factor: 0.5,
imprecision_factor: 0.9,
}
}
}
impl Parameters {
/// Change the Lipschitz-guess increase factor.
pub fn set_l_guess_factor(&mut self, l_guess_factor: Real) {
assert!(
l_guess_factor > 0.0 && l_guess_factor <= 1.0,
"Lipschitz-guess increase factor must be in (0,1], got: {}",
l_guess_factor
);
self.l_guess_factor = l_guess_factor;
}
/// Change the descent step acceptance factor.
///
/// The default value is 0.1.
pub fn set_acceptance_factor(&mut self, acceptance_factor: Real) {
assert!(
acceptance_factor > 0.0 && acceptance_factor < 1.0,
"Descent step acceptance factors must be in (0,1), got: {}",
acceptance_factor
);
self.acceptance_factor = acceptance_factor;
}
/// Change the imprecision acceptance factor.
///
/// The default value is 0.9.
pub fn set_imprecision_factor(&mut self, imprecision_factor: Real) {
assert!(
imprecision_factor > 0.0 && imprecision_factor < 1.0,
"Imprecision factor must be in (0,1), got: {}",
imprecision_factor
);
self.imprecision_factor = imprecision_factor;
}
}
/// The step type that has been performed.
#[derive(Clone, Copy, PartialEq, Eq, Debug)]
pub enum Step {
/// A null step has been performed.
Null,
/// A descent step has been performed.
Descent,
/// No step but the algorithm has been terminated.
Term,
}
pub struct SolverData<Mn, E> {
/// Current center of stability.
cur_y: Point,
/// Function value in the current point.
cur_val: Real,
/// Step direction (i.e. nxt_y - cur_y).
nxt_d: Arc<DVector>,
/// Current candidate.
nxt_y: Point,
/// Function value at the current candidate.
nxt_val: Real,
/// Model value at the current candidate.
nxt_mod: Real,
/// The current expected progress.
///
/// This value is actually `cur_val - nxt_val`. We store it separately only
/// for debugging purposes because after a descent step `cur_val` will be
/// changed and we could not see the "old" expected progress anymore that
/// led to the descent step.
expected_progress: Real,
/// Norm of current aggregated subgradient.
sgnorm: Real,
/// The error bound for the last descent step.
error_bound: Real,
/// The currently used master problem weight.
cur_weight: Real,
/// Maximal number of iterations.
max_iter: usize,
/// Number of inner model updates.
cnt_updates: usize,
/// Number of descent steps.
cnt_descent: usize,
/// The number of subproblems with insufficient evaluation data.
num_insufficient_candidates: usize,
/// The number of subproblems that have not been evaluated exactly in the center.
num_inexact_center: usize,
/// Whether only synchronized steps are accepted.
force_synchronized: bool,
/// Subproblem data.
subs: Vec<SubData<Mn, E>>,
/// The list of all evaluation points.
candidates: Vec<Point>,
/// Whether we need a new update
need_update: bool,
/// Whether a problem update is currently in progress.
update_in_progress: bool,
/// Whether the termination criterion has been satisfied but we wait for a
/// update.
pending_termination: bool,
}
impl<Mn, E> SolverData<Mn, E> {
/// Reset solver data to initial values.
///
/// This means that almost everything is set to +infinity so that
/// a null-step is forced after the first evaluation.
fn init(&mut self, y: Point) {
self.cnt_descent = 0;
self.cur_y = y.clone();
self.cur_val = Real::infinity();
self.nxt_y = y;
self.nxt_val = Real::infinity();
self.nxt_mod = -Real::infinity();
self.expected_progress = Real::infinity();
self.error_bound = Real::infinity();
self.candidates.clear();
self.sgnorm = Real::infinity();
self.cur_weight = 1.0;
self.num_insufficient_candidates = 0;
self.num_inexact_center = self.subs.len();
self.force_synchronized = false;
self.pending_termination = false;
}
}
impl<Mn, E> Default for SolverData<Mn, E> {
fn default() -> SolverData<Mn, E> {
SolverData {
cur_y: Point::default(),
cur_val: 0.0,
nxt_val: 0.0,
nxt_mod: 0.0,
expected_progress: 0.0,
sgnorm: 0.0,
error_bound: Real::infinity(),
cur_weight: 1.0,
num_insufficient_candidates: 0,
num_inexact_center: 0,
force_synchronized: false,
candidates: vec![],
max_iter: 0,
cnt_descent: 0,
cnt_updates: 0,
subs: vec![],
nxt_d: Arc::new(dvec![]),
nxt_y: Point::default(),
need_update: true,
update_in_progress: false,
pending_termination: false,
}
}
}
impl<Mn, E> StandardTerminatable for SolverData<Mn, E> {
fn center_value(&self) -> Real {
self.cur_val
}
fn expected_progress(&self) -> Real {
self.expected_progress
}
}
impl<Mn, E> HKWeightable for SolverData<Mn, E> {
fn current_weight(&self) -> Real {
self.cur_weight
}
fn center(&self) -> &DVector {
&self.cur_y.point
}
fn center_value(&self) -> Real {
self.cur_val
}
fn candidate_value(&self) -> Real {
self.nxt_val
}
fn candidate_model(&self) -> Real {
self.nxt_mod
}
fn new_cutvalue(&self) -> Real {
self.cur_val
}
fn sgnorm(&self) -> Real {
self.sgnorm
}
}
/// Data providing access for updating the problem.
struct UpdateData<Pr> {
/// Type of step.
step: Step,
/// Current center of stability.
cur_y: Arc<DVector>,
/// Current candidate.
nxt_y: Arc<DVector>,
/// The master process.
primal_aggrs: Vec<Pr>,
}
impl<Pr> UpdateState<Pr> for UpdateData<Pr>
where
Pr: Send + 'static,
{
fn was_descent(&self) -> bool {
self.step == Step::Descent
}
fn center(&self) -> &DVector {
&self.cur_y
}
fn candidate(&self) -> &DVector {
&self.nxt_y
}
fn aggregated_primal(&self, i: usize) -> &Pr {
&self.primal_aggrs[i]
}
}
/// Allowed change of the Lipschitz-constant.
#[derive(PartialEq, Clone, Copy, Debug)]
enum UpdateL {
/// L-constant is allowed to increase by the given factor.
Increase(Real),
/// L-constant is allowed to decrease by the given factor.
Decrease(Real),
}
/// Result of a model update.
enum UpdateResult {
/// The model has been modified.
Modified,
/// The model has not been modified.
Unmodified,
/// The pending termination has succeeded.
Term,
}
/// Model data of a single subproblem.
///
/// This struct does not handle the subproblem model itself. However, it handles
/// the asynchronous precision data, i.e. the guessed Lipschitz-constant and the
/// distance of the evaluation points to the candidate.
///
/// The concrete model used for computing the guessed values in the candidate
/// and the center must be provided by an implementation of `SubProblem`.
struct SubData<Mn, E> {
/// The index associated with this subproblem.
fidx: usize,
/// The subproblem.
sub: Box<dyn GuessModel<Mn, E>>,
/// The current center.
center: Point,
/// The current candidate.
candidate: Point,
/// The last index at which the evaluation has been started.
last_eval_index: usize,
/// Whether a subproblem evaluation is currently running.
is_running: bool,
/// Whether the last evaluation has been sufficiently close.
is_close_enough: bool,
/// The original guess value and its evaluation distance in the current center.
center_guess: Guess,
/// The current guess of the Lipschitz constant.
l_guess: Real,
/// Number of evaluations.
cnt_evals: usize,
}
impl<Mn, E> SubData<Mn, E> {
fn new(fidx: usize, sub: Box<dyn GuessModel<Mn, E>>, y: &Point) -> SubData<Mn, E> {
SubData {
fidx,
sub,
last_eval_index: 0,
center: y.clone(),
candidate: y.clone(),
is_running: false,
is_close_enough: false,
center_guess: Guess::default(),
l_guess: 0.0,
cnt_evals: 0,
}
}
/// Set the center of this model.
///
/// If `update_l_guess` is true also update the guess of the Lipschitz constant.
fn move_center(&mut self, y: &Point, l_guess_factor: UpdateL) {
assert_eq!(y.index, self.candidate.index, "Must move to current candidate");
// The guess value used in the current (i.e. old) center
let old_guess = self.center_guess;
// The cut value now known for the center.
let old_cutvalue = self.sub.get_lower_bound(&self.center);
// There has been a previous evaluation, so first update the Lipschitz guess ...
if let Some((l_guess_factor, increase)) = match l_guess_factor {
UpdateL::Increase(v) => Some((v, true)),
UpdateL::Decrease(v) => Some((v, false)),
} {
debug_assert!(
Some(l_guess_factor).map(|f| 0.0 < f && f <= 1.0).unwrap_or(true),
"Invalid l_guess_factor: {:?}",
l_guess_factor
);
if old_guess.dist > Real::zero() {
debug!(
"L-guess fidx:{} guess:{} cut:{}",
self.fidx, old_guess.value, old_cutvalue
);
let new_l_guess = if old_guess.dist.is_finite() {
(old_cutvalue - old_guess.value) / old_guess.dist
} else {
Real::zero()
};
if increase == (new_l_guess > self.l_guess) && new_l_guess >= 0.0 {
let new_l_guess = self.l_guess + l_guess_factor * (new_l_guess - self.l_guess);
debug!(
"New l_guess fidx:{} old-L:{} L:{}",
self.fidx, self.l_guess, new_l_guess,
);
self.l_guess = new_l_guess;
}
}
}
// Save the new center
self.center = y.clone();
// Save guess value of the candidate/new center
self.center_guess = self.sub.get_guess_value(&self.center);
}
/// Set the candidate of this model.
fn update_candidate(&mut self, y: &Point, accept_factor: Real) {
self.candidate = y.clone();
self.update_close_enough(accept_factor);
}
/// Add a function value to this model.
///
/// The `accept_factor` is a parameter for possibly accepting
/// the candidate guess value as "good enough" (if it has been
/// changed by the new minorant).
fn add_function_value(&mut self, y: &Point, value: Real, accept_factor: Real) {
self.sub.add_function_value(y, value);
self.update_close_enough(accept_factor)
}
/// Add a minorant to this model.
///
/// The `accept_factor` is a parameter for possibly accepting
/// the candidate guess value as "good enough" (if it has been
/// changed by the new minorant).
///
/// The minorant must be centered at the global 0.
fn add_minorant(&mut self, y: &Point, m: &Arc<Mn>, accept_factor: Real) {
self.sub.add_minorant(y, m);
self.update_close_enough(accept_factor)
}
/// Add some variables to this model.
fn add_vars(&mut self, nnew: usize, sgext: &dyn SubgradientExtender<Mn, E>) -> std::result::Result<(), E> {
Arc::make_mut(&mut self.center.point).extend(repeat(Real::zero()).take(nnew));
Arc::make_mut(&mut self.candidate.point).extend(repeat(Real::zero()).take(nnew));
self.sub.add_vars(nnew, sgext)
}
/// Return the current guess value at the given point.
fn get_guess_value(&mut self, y: &Point) -> Guess {
self.sub.get_guess_value(y)
}
/// Return the lower bound at the given point.
fn get_lower_bound(&mut self, y: &Point) -> Real {
self.sub.get_lower_bound(y)
}
fn update_close_enough(&mut self, accept_factor: Real) {
let g = self.sub.get_guess_value(&self.candidate);
self.is_close_enough = g.is_exact() || g.dist * self.l_guess <= accept_factor
}
/// Return the error estimation in the center.
///
/// This is the difference between the (current) lower bound and the used
/// guess value.
fn error_estimate(&mut self) -> Real {
self.sub.get_lower_bound(&self.center) - self.center_guess.value
}
fn center_guess_value(&self) -> Real {
self.center_guess.value
}
}
/// Implementation of a parallel bundle method.
pub struct Solver<P, T = StandardTerminator, W = HKWeighter, M = crate::master::FullMasterBuilder>
where
P: FirstOrderProblem,
M: MasterBuilder<P::Minorant>,
{
/// Parameters for the solver.
pub params: Parameters,
/// Termination predicate.
pub terminator: T,
/// Weighter heuristic.
pub weighter: W,
/// The threadpool of the solver.
pub threadpool: ThreadPool,
/// The master problem builder.
pub master: M,
/// The first order problem.
problem: P,
/// The algorithm data.
data: SolverData<P::Minorant, P::Err>,
/// The master problem process.
master_proc: Option<MasterProcess<P, M::MasterProblem>>,
/// Whether there is currently a master computation running.
master_running: bool,
/// Whether the master problem has been changed.
master_has_changed: bool,
/// The last subgradient extender.
sgext: Option<Arc<dyn SubgradientExtender<P::Minorant, P::Err>>>,
/// The channel to receive the evaluation results from subproblems.
client_tx: Option<ClientSender<EvalId, P, M::MasterProblem>>,
/// The channel to receive the evaluation results from subproblems.
client_rx: Option<ClientReceiver<EvalId, P, M::MasterProblem>>,
/// Time when the solution process started.
///
/// This is actually the time of the last call to `Solver::init`.
start_time: Instant,
}
impl<P, T, W, M> Solver<P, T, W, M>
where
P: FirstOrderProblem,
P::Err: Send,
T: Terminator<SolverData<P::Minorant, P::Err>> + Default,
W: Weighter<SolverData<P::Minorant, P::Err>> + Default,
M: MasterBuilder<P::Minorant>,
{
/// Create a new parallel bundle solver.
pub fn new(problem: P) -> Self
where
M: Default,
{
Self::with_master(problem, M::default(), Parameters::default())
}
/// Create a new parallel bundle solver with given parameters.
pub fn with_params(problem: P, params: Parameters) -> Self
where
M: Default,
{
Self::with_master(problem, M::default(), params)
}
/// Create a new parallel bundle solver with master problem and parameters.
pub fn with_master(problem: P, master: M, params: Parameters) -> Self {
let ncpus = num_cpus::get();
info!("Initializing asynchronous solver with {} CPUs", ncpus);
Solver {
params,
terminator: Default::default(),
weighter: Default::default(),
problem,
data: SolverData::default(),
threadpool: ThreadPool::with_name("Parallel bundle solver".to_string(), ncpus),
master,
master_proc: None,
master_has_changed: false,
master_running: false,
sgext: None,
client_tx: None,
client_rx: None,
start_time: Instant::now(),
}
}
/// Return the underlying threadpool.
///
/// In order to use the same threadpool for concurrent processes,
/// just clone the returned `ThreadPool`.
pub fn threadpool(&self) -> &ThreadPool {
&self.threadpool
}
/// Set the threadpool.
///
/// This function allows to use a specific threadpool for all processes
/// spawned by the solver. Note that this does not involve any threads
/// used by the problem because the solver is not responsible for executing
/// the evaluation process of the subproblems. However, the problem might
/// use the same threadpool as the solver.
pub fn set_threadpool(&mut self, threadpool: ThreadPool) {
self.threadpool = threadpool;
}
/// Return the current problem associated with the solver.
pub fn problem(&self) -> &P {
&self.problem
}
/// Return the current problem associated with the solver.
pub fn into_problem(self) -> P {
self.problem
}
/// Initialize the solver.
///
/// This will reset the internal data structures so that a new fresh
/// solution process can be started.
///
/// It will also setup all worker processes.
///
/// This function is automatically called by [`Solver::solve`].
pub fn init(&mut self) -> Result<(), P, M> {
debug!("Initialize solver");
debug!("{:?}", self.params);
let n = self.problem.num_variables();
let m = self.problem.num_subproblems();
let (tx, rx) = channel();
self.client_tx = Some(tx.clone());
self.client_rx = Some(rx);
let lower_bounds = self.problem.lower_bounds().map(DVector);
if lower_bounds.as_ref().map(|lb| lb.len() != n).unwrap_or(false) {
return Err(Error::Dimension {
what: "lower bounds".to_string(),
});
}
let upper_bounds = self.problem.upper_bounds().map(DVector);
if upper_bounds.as_ref().map(|ub| ub.len() != n).unwrap_or(false) {
return Err(Error::Dimension {
what: "upper bounds".to_string(),
});
}
let mut constraint_sets = HashMap::new();
for i in 0..n {
if let Some(k) = self.problem.constraint_index(i) {
if lower_bounds.as_ref().map(|lb| lb[i] != 0.0).unwrap_or(true) {
return Err(Error::InvalidConvexityLowerBound(i));
}
if upper_bounds
.as_ref()
.map(|ub| ub[i] != Real::infinity())
.unwrap_or(false)
{
return Err(Error::InvalidConvexityUpperBound(i));
}
constraint_sets.entry(k).or_insert_with(|| vec![]).push(i);
}
}
// compute initial y
let mut init_y = dvec![Real::zero(); n];
for convexity_set in constraint_sets.values() {
let value = Real::one() / Real::from_usize(convexity_set.len()).unwrap();
for &i in convexity_set {
init_y[i] = value;
}
}
self.data.init(Point {
index: 1,
point: Arc::new(init_y),
});
debug!("Start master process");
let mut master = self.master.build().map_err(Error::BuildMaster)?;
master.set_num_subproblems(m).map_err(Error::BuildMaster)?;
master
.set_vars(n, lower_bounds, upper_bounds)
.map_err(Error::BuildMaster)?;
master.set_convexity_sets(constraint_sets.into_values());
// we must set the center correctly
master.move_center(1.0, &self.data.cur_y.point);
self.master_proc = Some(MasterProcess::start(master, tx, &mut self.threadpool));
debug!("Initial problem evaluation");
// The initial evaluation point.
self.data.nxt_y = self.data.cur_y.clone();
// We need an initial evaluation of all oracles for the first center.
info!("Guess model type: {:?}", self.params.guess_model);
self.data.subs = (0..m)
.map(|fidx| {
SubData::new(
fidx,
match self.params.guess_model {
GuessModelType::NearestValue => Box::new(NearestValue::new(fidx)),
GuessModelType::CutModel => Box::new(CutModel::new(fidx)),
},
&self.data.cur_y,
)
})
.collect();
// This could be done better: the initial evaluation has index 1!
self.data.candidates.push(self.data.nxt_y.clone());
self.data.candidates.push(self.data.nxt_y.clone());
for i in 0..m {
self.evaluate_subproblem(i)?;
}
self.start_time = Instant::now();
// wait for all subproblem evaluations.
let mut cnt_remaining = self.problem.num_subproblems();
let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
let client_rx = self.client_rx.as_ref().ok_or(Error::NotInitialized)?;
while cnt_remaining > 0 {
let msg = client_rx.recv();
match msg? {
Message::Eval(m) => match m {
EvalResult::ObjectiveValue { index, value } => {
assert_eq!(
index.candidate_index, self.data.nxt_y.index,
"Receive objective value for unexpected candidate"
);
self.data.subs[index.subproblem].add_function_value(&self.data.nxt_y, value, 0.0);
}
EvalResult::Minorant { index, mut minorant } => {
assert_eq!(
index.candidate_index, self.data.nxt_y.index,
"Receive objective value for unexpected candidate"
);
// Add the minorant to the master problem.
// The minorant is centered at the candidate == center, so it does
// not need to be moved.
master.add_minorant(index.subproblem, minorant.clone())?;
self.master_has_changed = true;
// Center the minorant at 0.
minorant.move_center(-1.0, &self.data.nxt_y.point);
self.data.subs[index.subproblem].add_minorant(&self.data.nxt_y, &Arc::new(minorant), 0.0);
}
EvalResult::Done { index } => {
self.data.subs[index.subproblem].is_running = false;
cnt_remaining -= 1;
}
EvalResult::Error { err, index } => {
self.data.subs[index.subproblem].is_running = false;
return Err(Error::Evaluation(err));
}
},
Message::Update(_) => {
unreachable!("Receive update response during initialization");
}
Message::Master(_) => {
unreachable!("Receive master response during initialization");
}
}
}
// Set the initial values.
// For the center this is the current lower bound (cut value),
// for the candidate it is the model candidate value.
//
// Note that both are the same ...
self.data.cur_val = 0.0;
self.data.nxt_val = 0.0;
for s in &mut self.data.subs {
self.data.cur_val += s.get_lower_bound(&self.data.nxt_y);
self.data.nxt_val += s.get_guess_value(&self.data.nxt_y).value;
}
assert!((self.data.cur_val - self.data.nxt_val).abs() < 1e-6);
// This is the first evaluation. We effectively get
// the function value at the current center but
// we do not have a model estimate yet. Hence, we do not know
// a good guess for the weight.
self.data.cur_weight = Real::infinity();
self.data.need_update = true;
// Compute the initial candidate
self.update_candidate()?;
if let Ok(msg) = self.client_rx.as_ref().ok_or(Error::NotInitialized)?.recv() {
if let Message::Master(m) = msg {
self.handle_master_response(m)?;
} else {
unreachable!(
"Unexpected non-master response during initial candidate computation: {:?}",
msg
);
}
} else {
todo!("What to do with this error?");
}
// Start an initial problem update.
self.update_problem(Step::Descent)?;
debug!("First Step");
debug!(" cur_val={}", self.data.cur_val);
self.data.cnt_descent += 1;
self.show_info(Step::Descent);
debug!("Initialization complete");
Ok(())
}
/// Reset data for new iterations.
fn reset_iteration_data(&mut self, max_iter: usize) {
let num_variables = self.problem.num_variables();
self.data.max_iter = max_iter;
self.data.cnt_updates = 0;
self.data.nxt_d = Arc::new(dvec![0.0; num_variables]);
self.data.nxt_y = self.data.cur_y.clone();
let nxt_y = &self.data.nxt_y;
self.data.nxt_val = self
.data
.subs
.iter_mut()
.map(|s| s.get_guess_value(nxt_y).value)
.sum::<Real>();
self.data.need_update = true;
// TODO: there might be an update in progress -> the 'DONE' message should be
// in the message queue ...
//self.data.update_in_progress = false;
}
/// Solve the problem with the default maximal iteration limit [`DEFAULT_ITERATION_LIMIT`].
pub fn solve(&mut self) -> Result<(), P, M> {
self.solve_with_limit(DEFAULT_ITERATION_LIMIT)
}
/// Solve the problem with a maximal iteration limit.
pub fn solve_with_limit(&mut self, limit: usize) -> Result<(), P, M> {
// First initialize the internal data structures.
self.init()?;
if self.solve_iter(limit)? {
Ok(())
} else {
Err(Error::IterationLimit { limit })
}
}
/// Solve the problem but stop after at most `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, P, M> {
debug!("Start solving up to {} iterations", niter);
self.reset_iteration_data(niter);
loop {
let msg = self.client_rx.as_ref().ok_or(Error::NotInitialized)?.recv()?;
match msg {
Message::Eval(m) => {
// Receive a evaluation result
if self.handle_client_response(m)? {
return Ok(true);
}
}
Message::Update(msg) => {
debug!("Receive update response");
match self.handle_update_response(msg)? {
UpdateResult::Modified => {
// The master problem has been changed so we need a new
// candidate as well.
eprintln!("New candidate?");
self.update_candidate()?;
}
UpdateResult::Unmodified => (),
UpdateResult::Term => return Ok(true),
}
}
Message::Master(mresponse) => {
debug!("Receive master response");
// Receive result (new candidate) from the master
if self.handle_master_response(mresponse)? {
return Ok(true);
}
}
}
}
}
/// Handle a response `msg` from a subproblem evaluation.
///
/// The result from a subproblem evaluation is either a (upper bound on the)
/// function value is some point or a subgradient (or linear minorant) on
/// the function in some point.
///
/// The current implementation will wait until all subproblems have created
/// at least one function value and one subgradient. Once this is true,
/// either a descent step or a null step is performed depending on the
/// outcome of the descent test.
///
/// After the step the problem gets a chance to update (e.g. generate new
/// variables), and the master problem is started to compute a new
/// candidate.
///
/// The function returns
/// - `Ok(true)` if the final iteration count has been reached,
/// - `Ok(false)` if the final iteration count has not been reached,
/// - `Err(_)` on error.
fn handle_client_response(&mut self, msg: EvalResult<EvalId, P::Minorant, P::Err>) -> Result<bool, P, M> {
match msg {
EvalResult::ObjectiveValue { index, value } => self.handle_new_objective(index, value),
EvalResult::Minorant { index, minorant } => self.handle_new_minorant(index, minorant),
EvalResult::Done { index } => {
let sub = &mut self.data.subs[index.subproblem];
sub.is_running = false;
// possibly restart the subproblem for the current candidate
self.evaluate_subproblem(index.subproblem)?;
Ok(false)
}
EvalResult::Error { err, index } => {
self.data.subs[index.subproblem].is_running = false;
Err(Error::Evaluation(err))
}
}
}
/// A new objective value has been computed.
fn handle_new_objective(&mut self, id: EvalId, value: Real) -> Result<bool, P, M> {
debug!(
"Receive objective from subproblem:{} candidate:{} current:{} obj:{}",
id.subproblem, id.candidate_index, self.data.nxt_y.index, value
);
// check if new evaluation is closer to the current candidate
let sub = &mut self.data.subs[id.subproblem];
// Whether the subproblem evaluation had been good enough.
let was_close_enough = sub.is_close_enough;
if let Some(nxt) = self.data.candidates.get_mut(id.candidate_index) {
let accept_factor =
self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
/ self.problem.num_subproblems().to_f64().unwrap();
let old_can_val = sub.get_guess_value(&self.data.nxt_y).value;
let old_cen_val = sub.get_lower_bound(&self.data.cur_y);
sub.add_function_value(nxt, value, accept_factor);
let new_can_val = sub.get_guess_value(&self.data.nxt_y).value;
let new_cen_val = sub.get_lower_bound(&self.data.cur_y);
self.data.nxt_val += new_can_val - old_can_val;
self.data.cur_val += new_cen_val - old_cen_val;
// This is just a safe-guard: if the function has been evaluated at
// the current candidate, the evaluation *must* be good enough.
//
// Unfortunately, that safe-guard does not work with guess-models that do not use the function value (like `CutModel`) ....
//
// assert!(
// sub.is_close_enough || sub.last_eval_index < self.data.nxt_y.index,
// "Unexpected insufficiency fidx:{} l:{}",
// id.subproblem,
// sub.l_guess,
// );
} else {
// unknown candidate -> ignore objective value
warn!("Ignore unknown candidate index:{}", id.candidate_index);
return Ok(false);
}
// Test if the new candidate is close enough for the asynchronous
// precision test.
if !was_close_enough && sub.is_close_enough {
debug!(
"Accept result fidx:{} index:{} candidate:{} (remaining insufficient: {})",
id.subproblem, id.candidate_index, self.data.nxt_y.index, self.data.num_insufficient_candidates
);
}
self.maybe_do_step(false)
}
/// Add a new minorant.
///
/// The minorant is added to the submodel as well as the master problem. The modified submodel
/// may then lead to a potential null/descent-step.
///
/// Return values
/// - `Ok(true)` if the termination criterion has been satisfied,
/// - `Ok(false)` if the termination criterion has not been satisfied,
/// - `Err(_)` on error.
fn handle_new_minorant(&mut self, id: EvalId, mut minorant: P::Minorant) -> Result<bool, P, M> {
debug!(
"Receive minorant subproblem:{} candidate:{} current:{} center:{}",
id.subproblem, id.candidate_index, self.data.nxt_y.index, self.data.cur_y.index,
);
// First check if the minorant needs to be extended.
if let Some(ref sgext) = self.sgext {
if minorant.dim() < self.data.cur_y.point.len() {
sgext
.extend_subgradient(id.subproblem, &mut minorant)
.map_err(Error::SubgradientExtension)?;
}
}
let accept_factor =
self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
/ self.problem.num_subproblems().to_f64().unwrap();
let sub = &mut self.data.subs[id.subproblem];
let mut minorant = minorant;
if let Some(point) = self.data.candidates.get_mut(id.candidate_index) {
// center the minorant at 0
minorant.move_center(-1.0, &point.point);
// add minorant to submodel
let old_can_val = sub.get_guess_value(&self.data.nxt_y).value;
let old_cen_val = sub.get_lower_bound(&self.data.cur_y);
sub.add_minorant(point, &Arc::new(minorant.clone()), accept_factor);
let new_can_val = sub.get_guess_value(&self.data.nxt_y).value;
let new_cen_val = sub.get_lower_bound(&self.data.cur_y);
self.data.nxt_val += new_can_val - old_can_val;
self.data.cur_val += new_cen_val - old_cen_val;
// center the minorant at the current center
minorant.move_center(1.0, &self.data.cur_y.point);
} else {
warn!("Ignore unknown candidate index:{}", id.candidate_index);
return Ok(false);
}
// add minorant to master problem
let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
master.add_minorant(id.subproblem, minorant)?;
self.master_has_changed = true;
self.maybe_do_step(false)
}
/// Handle a response `master_res` from the master problem process.
///
/// The master response is the new candidate point. The method updates the
/// algorithm state with the data of the new candidate (e.g. the model value
/// `nxt_mod` in the point or the expected progress). Then it tests whether
/// a termination criterion is satisfied. This is only the case if there is
/// no pending problem update.
///
/// Finally the master problem starts the evaluation of all subproblems at
/// the new candidate.
///
/// The new candidate is immediately checked for a potential new test.
///
/// Return values
/// - `Ok(true)` if the termination criterion has been satisfied,
/// - `Ok(false)` if the termination criterion has not been satisfied,
/// - `Err(_)` on error.
fn handle_master_response(&mut self, master_res: MasterResponse<P, M::MasterProblem>) -> Result<bool, P, M> {
match master_res {
Response::Error(err) => return Err(err.into()),
Response::Result {
nxt_mod,
sgnorm,
cnt_updates,
nxt_d,
center_index,
} => {
self.data.nxt_d = Arc::new(nxt_d);
self.data.nxt_mod = nxt_mod;
self.data.sgnorm = sgnorm;
self.data.cnt_updates = cnt_updates;
debug!(
"Master Response current_center:{} current_candidate:{} res_center:{} nxt_mod:{} sgnorm:{}",
self.data.cur_y.index, self.data.nxt_y.index, center_index, self.data.nxt_mod, self.data.sgnorm
);
}
};
self.data.expected_progress = self.data.cur_val - self.data.nxt_mod;
self.master_running = false;
let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
// If this is the very first solution of the model,
// we use its result as to make a good guess for the initial weight
// of the proximal term and resolve.
if self.data.cur_weight.is_infinite() {
self.data.cur_weight = self.weighter.initial_weight(&self.data);
master.set_weight(self.data.cur_weight)?;
self.master_has_changed = true;
self.update_candidate()?;
return Ok(false);
}
// Compress bundle
master.compress()?;
// Check if new variables had been added. In this case, resize cur_y.
match self.data.nxt_d.len().cmp(&self.data.cur_y.point.len()) {
Ordering::Greater => {
let nnew = self.data.nxt_d.len() - self.data.cur_y.point.len();
Arc::make_mut(&mut self.data.cur_y.point).extend(repeat(Real::zero()).take(nnew));
}
Ordering::Less => {
let nnew = self.data.cur_y.point.len() - self.data.nxt_d.len();
Arc::make_mut(&mut self.data.nxt_d).extend(repeat(Real::zero()).take(nnew));
}
_ => (),
}
if self.data.sgnorm.is_zero() {
return Ok(false);
}
// Compute new candidate.
let mut next_y = dvec![];
next_y.add(&self.data.cur_y.point, &self.data.nxt_d);
#[cfg(debug_assertions)]
{
if self.data.nxt_y.point.len() == next_y.len() {
let mut diff = self.data.nxt_y.point.as_ref().clone();
diff.add_scaled(-1.0, &next_y);
debug!("Candidates move distance:{}", diff.norm2());
}
}
self.data.nxt_y.point = Arc::new(next_y);
self.data.nxt_y.index += 1;
// Add the new candidate to the list of candidates.
debug_assert_eq!(self.data.nxt_y.index, self.data.candidates.len());
self.data.candidates.push(self.data.nxt_y.clone());
// Update the candidate in all submodels and
// compute first guess value for new candidate.
let accept_factor =
self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
/ self.problem.num_subproblems().to_f64().unwrap();
self.data.nxt_val = Real::zero();
for sub in self.data.subs.iter_mut() {
sub.update_candidate(&self.data.nxt_y, accept_factor);
self.data.nxt_val += sub.get_guess_value(&self.data.nxt_y).value;
}
// Start evaluation of all subproblems at the new candidate.
for i in 0..self.data.subs.len() {
self.evaluate_subproblem(i)?;
}
self.maybe_do_step(true)
}
/// Do a descent or null step if precision is sufficient.
///
/// Also checks if the termination criterion is satisfied.
///
/// Return values
/// - `Ok(true)` if the termination criterion has been satisfied,
/// - `Ok(false)` if the termination criterion has not been satisfied,
/// - `Err(_)` on error.
fn maybe_do_step(&mut self, check_termination: bool) -> Result<bool, P, M> {
// No step if there is no real new candidate
if self.data.nxt_y.index == self.data.cur_y.index {
return Ok(false);
}
self.data.num_insufficient_candidates = self.data.subs.iter().filter(|s| !s.is_close_enough).count();
let nxt_y = &self.data.nxt_y;
// A synchronized step requires exact candidates.
if self.data.force_synchronized {
let num_exact = self
.data
.subs
.iter_mut()
.map(|s| s.get_guess_value(nxt_y).is_exact())
.filter(|&is_exact| is_exact)
.count();
if num_exact < self.data.subs.len() {
// Inexact candidate -> do not compute a new candidate but wait
// for all evaluations.
return Ok(false);
}
}
let num_exact = self
.data
.subs
.iter_mut()
.map(|s| s.get_guess_value(nxt_y).is_exact())
.filter(|&is_exact| is_exact)
.count();
let sum_dist = self
.data
.subs
.iter_mut()
.map(|s| s.get_guess_value(nxt_y).dist)
.sum::<Real>();
// TODO: maybe we should wait for *minorants* to be exact in the
// candidate, so that the guess-value and the lower bound in the
// candidate are guaranteed to be equal? (This might be relevant for the
// termination test ... I'm not sure about this right now ...)
debug!(
"Number of insufficient subproblems: {} num exact: {} sum dist: {}",
self.data.num_insufficient_candidates, num_exact, sum_dist,
);
if log_enabled!(Debug) {
let accept_factor =
self.params.imprecision_factor * self.params.acceptance_factor * self.data.expected_progress
/ self.problem.num_subproblems().to_f64().unwrap();
for (fidx, sub) in self.data.subs.iter_mut().enumerate() {
debug!(
" {}sufficient fidx:{} dist:{} required:{}",
if sub.is_close_enough { " " } else { "in" },
fidx,
sub.get_guess_value(nxt_y).dist,
if sub.l_guess.is_zero() {
Real::infinity()
} else {
accept_factor / sub.l_guess
}
);
}
}
// test if we should terminate
if check_termination && self.terminator.terminate(&self.data) && self.data.cnt_descent > 2 {
// Terminate only with exact center.
if self.data.num_inexact_center.is_zero() {
self.show_info(Step::Term);
if self.data.update_in_progress || self.data.need_update {
info!("Termination criterion satisfied, waiting for pending update");
self.data.pending_termination = true;
} else {
info!("Termination criterion satisfied");
return Ok(true);
}
} else {
// The termination criterion has been satisfied, but the current
// center evaluations are not exact. We force the current
// *candidate* evaluations to be exact and do a forced descent step.
// This causes the next center to be exact and hopefully satisfying
// the termination criterion, too.
let num_inexact = self.data.subs.len() - num_exact;
debug!(
"Termination criterion satisfied with {} inexact evaluations",
num_inexact
);
// Inexact candidates -> force a synchronized step.
if num_inexact > 0 {
debug!("Switch to synchronized mode");
self.data.force_synchronized = true;
return Ok(false);
}
}
}
// Do a regular, maybe inexact, step.
if self.data.num_insufficient_candidates == 0 {
self.do_step()
} else {
Ok(false)
}
}
/// Do a null-step or descent-step based on the current candidate data.
fn do_step(&mut self) -> Result<bool, P, M> {
let master = self.master_proc.as_mut().ok_or(Error::NotInitialized)?;
// In a forced synchronized step, any descent is accepted ...
let descent_bnd = if !self.data.force_synchronized {
Self::get_descent_bound(self.params.acceptance_factor, &self.data)
} else {
self.data.cur_val
};
debug!(
"Try step from center:{} to candidate:{}",
self.data.cur_y.index, self.data.nxt_y.index
);
// Test whether we do a descent step
if self.data.nxt_val <= descent_bnd {
debug!("Descent Step");
debug!(" cur_val ={}", self.data.cur_val);
debug!(" nxt_mod ={}", self.data.nxt_mod);
debug!(" nxt_ub ={}", self.data.nxt_val);
debug!(" descent_bnd={}", descent_bnd);
// After a successful descent step, switch back to non-synchronized
if self.data.force_synchronized {
debug!("Switch to unsynchronized mode");
}
self.data.force_synchronized = false;
self.data.cnt_descent += 1;
self.data.cur_y = self.data.nxt_y.clone();
let cur_y = &self.data.cur_y;
self.data.num_inexact_center = self
.data
.subs
.iter_mut()
.map(|s| !s.get_guess_value(cur_y).is_exact())
.filter(|&is_exact| is_exact)
.count();
// Note that we must update the weight *before* we
// change the internal data, so the old information
// that caused the descent step is still available.
self.data.cur_weight = self.weighter.descent_weight(&self.data);
// The new value in the center is the model value in the candidate.
// In particular, it is a lower bound on the real function value.
self.data.cur_val = self
.data
.subs
.iter_mut()
.map(|s| s.get_lower_bound(cur_y))
.sum::<Real>();
// Check if the progress of the last decent step was large enough
// when using the lower bound in the center instead of the former
// guess value.
let error = self.data.subs.iter_mut().map(SubData::error_estimate).sum::<Real>();
let update_l_guess = error > self.data.error_bound;
// save new error bound
self.data.error_bound = self.data.expected_progress * self.params.acceptance_factor;
// Move all subproblems.
self.data.nxt_val = Real::zero();
for sub in &mut self.data.subs {
sub.move_center(
&self.data.cur_y,
if update_l_guess {
UpdateL::Increase(self.params.l_guess_factor)
} else {
UpdateL::Decrease(self.params.l_guess_factor * 0.5)
},
);
self.data.nxt_val += sub.get_guess_value(&self.data.nxt_y).value;
}
self.data.need_update = true;
master.move_center(1.0, self.data.nxt_d.clone(), self.data.cur_y.index)?;
master.set_weight(self.data.cur_weight)?;
self.master_has_changed = true;
self.show_info(Step::Descent);
self.update_problem(Step::Descent)?;
// We need a new candidate.
self.update_candidate()?;
Ok(self.data.cnt_descent >= self.data.max_iter)
} else {
debug!("Null Step nxt_val:{} descent_bnd:{}", self.data.nxt_val, descent_bnd);
// No descent-step, so this is declared a null step
self.data.cur_weight = self.weighter.null_weight(&self.data);
self.show_info(Step::Null);
self.update_problem(Step::Null)?;
// After a null step we need a new candidate, too.
self.update_candidate()?;
Ok(false)
}
}
/// Start evaluation of a subproblem if it is not running.
///
/// The evaluation is started at the current candidate. The candidate
/// is added to the subproblem's candidate list.
///
/// Returns `true` iff a new evaluations has been started.
fn evaluate_subproblem(&mut self, subproblem: usize) -> Result<bool, P, M> {
let sub = &mut self.data.subs[subproblem];
if !sub.is_running && sub.last_eval_index < self.data.nxt_y.index {
sub.is_running = true;
sub.last_eval_index = sub.last_eval_index.max(self.data.nxt_y.index);
self.problem
.evaluate(
subproblem,
self.data.nxt_y.point.clone(),
ChannelResultSender::new(
EvalId {
subproblem,
candidate_index: self.data.nxt_y.index,
},
self.client_tx.as_ref().ok_or(Error::NotInitialized)?.clone(),
),
)
.map_err(Error::Evaluation)?;
sub.cnt_evals += 1;
debug!("Evaluate fidx:{} candidate:{}", subproblem, self.data.nxt_y.index);
Ok(true)
} else {
assert!(sub.is_running || sub.is_close_enough);
Ok(false)
}
}
/// Possibly start a new master process computation.
fn update_candidate(&mut self) -> Result<(), P, M> {
if !self.master_running && self.master_has_changed {
debug!("Start master problem");
self.master_running = true;
self.master_has_changed = false;
self.master_proc
.as_mut()
.ok_or(Error::NotInitialized)?
.solve(self.data.cur_val)?;
}
Ok(())
}
/// Update the problem after descent or null `step`.
///
/// After a successful descent or null step the problem gets a chance to
/// update, i.e. to change some problem properties like adding new
/// variables. This method starts the update process.
///
/// Return values
/// - `Ok(true)` if a new update process has been started,
/// - `Ok(false)` if there is already a running update process (only one
/// is allowed at the same time),
/// - `Err(_)` on error.
fn update_problem(&mut self, step: Step) -> Result<bool, P, M> {
// only one update may be running at the same time
if self.data.update_in_progress {
return Ok(false);
}
// Ok, we are doing a new update now ...
self.data.need_update = false;
let master_proc = self.master_proc.as_mut().unwrap();
self.problem
.update(
UpdateData {
cur_y: self.data.cur_y.point.clone(),
nxt_y: self.data.nxt_y.point.clone(),
step,
primal_aggrs: (0..self.problem.num_subproblems())
.map(|i| master_proc.get_aggregated_primal(i))
.map(|p| p.map_err(Error::from))
.collect::<std::result::Result<Vec<_>, _>>()?,
},
ChannelUpdateSender::new(
EvalId {
subproblem: 0,
candidate_index: self.data.cur_y.index,
},
self.client_tx.clone().ok_or(Error::NotInitialized)?,
),
)
.map_err(Error::Update)?;
self.data.update_in_progress = true;
Ok(true)
}
/// Handles an update response `update` of the problem.
///
/// The method is called if the problem informs the solver about a change of
/// the problem, e.g. adding a new variable. This method informs the other
/// parts of the solver, e.g. the master problem, about the modification.
///
/// The method returns `Ok(true)` if the master problem has been modified.
fn handle_update_response(&mut self, update: Update<EvalId, P::Minorant, P::Err>) -> Result<UpdateResult, P, M> {
let mut modified = false;
match update {
Update::AddVariables { bounds, sgext, .. } => {
if !bounds.is_empty() {
eprintln!("Add variables {}", bounds.len());
let sgext: Arc<dyn SubgradientExtender<_, _>> = sgext.into();
// add new variables
let nnew = bounds.len();
self.master_proc
.as_mut()
.unwrap()
.add_vars(bounds.into_iter().map(|(l, u)| (l, u)).collect(), sgext.clone())?;
self.master_has_changed = true;
for sub in &mut self.data.subs {
sub.add_vars(nnew, sgext.as_ref())
.map_err(Error::SubgradientExtension)?;
}
self.sgext = Some(sgext);
let n = self.data.cur_y.point.len() + nnew;
Arc::make_mut(&mut self.data.cur_y.point).resize(n, Real::zero());
Arc::make_mut(&mut self.data.nxt_y.point).resize(n, Real::zero());
for c in &mut self.data.candidates {
Arc::make_mut(&mut c.point).resize(n, Real::zero());
}
modified = true;
if self.data.pending_termination {
info!("Cancel pending termination due to problem update");
self.data.pending_termination = false;
}
}
}
Update::Done { .. } => {
// currently we do nothing ...
self.data.update_in_progress = false;
// Pending termination?
if self.data.pending_termination {
info!("No update after termination criterion has been satisfied, so terminate");
return Ok(UpdateResult::Term);
}
}
Update::Error { err, .. } => {
self.data.update_in_progress = false;
return Err(Error::Update(err));
}
}
Ok(if modified {
UpdateResult::Modified
} else {
UpdateResult::Unmodified
})
}
/// Return the bound the function value must be below of to enforce a descent step.
///
/// If the oracle guarantees that $f(\bar{y}) \le$ this bound, the
/// bundle method will perform a descent step.
///
/// This value is $f(\hat{y}) + \varrho \cdot \Delta$ where
/// $\Delta = f(\hat{y}) - \hat{f}(\bar{y})$ is the expected
/// progress and $\varrho$ is the `acceptance_factor`.
fn get_descent_bound(acceptance_factor: Real, data: &SolverData<P::Minorant, P::Err>) -> Real {
data.cur_val - acceptance_factor * (data.cur_val - data.nxt_mod)
}
/// Log some information about the latest `step`.
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}({:9.4}) \
{:12.6}({:12.6}) {:12.6}({:12.6})",
if step == Step::Term { "_endit" } else { "endit " },
time.as_secs() / 3600,
(time.as_secs() / 60) % 60,
time.as_secs() % 60,
time.subsec_nanos() / 10_000_000,
self.data.cnt_descent,
self.data.cnt_updates,
self.data.subs.iter().map(|s| s.cnt_evals).sum::<usize>(),
if step == Step::Descent { "*" } else { " " },
PrettyPrintFloat(self.data.cur_weight),
PrettyPrintFloat(self.data.expected_progress()),
PrettyPrintFloat(self.data.cur_val - self.data.nxt_val),
PrettyPrintFloat(self.data.nxt_mod),
PrettyPrintFloat(self.data.nxt_val),
PrettyPrintFloat(self.data.cur_val),
PrettyPrintFloat(self.data.subs.iter().map(|s| s.center_guess_value()).sum::<Real>()),
);
}
/// Return the aggregated primal of the given subproblem.
pub fn aggregated_primal(&self, subproblem: usize) -> Result<<P::Minorant as Minorant>::Primal, P, M> {
Ok(self
.master_proc
.as_ref()
.ok_or(Error::NotSolved)?
.get_aggregated_primal(subproblem)?)
}
}