/*
* Copyright (c) 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
* 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 first-order oracle.
use crate::{Aggregatable, DVector, Minorant, Real};
use crossbeam::channel::Sender;
use std::sync::Arc;
/// Evaluation result.
///
/// The result of an evaluation is new information to be made
/// available to the solver and the master problem. There are
/// essentially two types of information:
///
/// 1. The (exact) function value of a sub-function at some point.
/// 2. A minorant of some sub-function.
#[derive(Debug)]
pub enum EvalResult<I, P> {
/// The objective value at some point.
ObjectiveValue { index: I, value: Real },
/// A minorant with an associated primal.
Minorant { index: I, minorant: Minorant, primal: P },
}
/// Channel to send evaluation results to.
pub type ResultSender<I, P, E> = Sender<Result<EvalResult<I, P>, E>>;
/// Problem update information.
///
/// The solver calls the `update` method of the problem regularly.
/// This method can modify the problem by adding (or moving)
/// variables. The possible updates are encoded in this type.
pub enum Update<I, P, E> {
/// Add new variables with bounds.
///
/// The initial value of each variable will be the feasible value
/// closest to 0.
AddVariables {
index: I,
bounds: Vec<(Real, Real)>,
sgext: Box<SubgradientExtender<P, E>>,
},
}
/// The subgradient extender is a callback used to update existing minorants
/// given their associated primal data.
pub type SubgradientExtender<P, E> = dyn FnMut(usize, &P, &[usize]) -> Result<DVector, E> + Send;
/// This trait provides information available in the
/// [`FirstOrderProblem::update`] method.
pub trait UpdateState<P> {
/// Whether the last step was a descent step.
fn was_descent(&self) -> bool;
/// Whether the last step was a null step.
fn was_null(&self) -> bool {
!self.was_descent()
}
/// The (old) current center of stability.
fn center(&self) -> Arc<DVector>;
/// The candidate point.
///
/// After a descent step, i.e. if [`UpdateState::was_descent`] is `true`,
/// this is the new center of stability.
fn candidate(&self) -> Arc<DVector>;
/// The current aggregated primal information.
///
/// Return the aggregated primal information for the given subproblem.
fn aggregated_primal(&self, i: usize) -> Arc<P>;
}
/// Channel to send problem updates to.
pub type UpdateSender<I, P, E> = Sender<Result<Update<I, P, E>, E>>;
/// Trait for implementing a first-order problem description.
///
/// All computations made by an implementation are supposed to
/// be asynchronous. Hence, the interface is slightly different
/// compared with [`crate::FirstOrderProblem`].
pub trait FirstOrderProblem {
/// Error raised by this oracle.
type Err;
/// The primal information associated with a minorant.
type Primal: Aggregatable + Send + 'static;
/// Return the number of variables.
fn num_variables(&self) -> usize;
/// Return the lower bounds on the variables.
///
/// If no lower bounds a specified, $-\infty$ is assumed.
///
/// The lower bounds must be less then or equal the upper bounds.
fn lower_bounds(&self) -> Option<Vec<Real>> {
None
}
/**
* Return the upper bounds on the variables.
*
* If no lower bounds a specified, $+\infty$ is assumed.
*
* The upper bounds must be greater than or equal the upper bounds.
*/
fn upper_bounds(&self) -> Option<Vec<Real>> {
None
}
/// Return the number of subproblems.
fn num_subproblems(&self) -> usize {
1
}
/// Start background processes.
///
/// This method is called right before the solver starts the solution process.
/// It can be used to setup any background tasks required for the evaluation
/// of the subfunctions.
///
/// Remember that background processes should be cleanup when the problem
/// is deleted (e.g. by implementing the [`Drop`] trait).
///
/// The default implementation does nothing.
fn start(&mut self) {}
/// Stop background processes.
///
/// This method is called right after the solver stops the solution process.
/// It can be used to stop any background tasks required for the evaluation
/// of the subfunctions.
///
/// A correct implementation of should cleanup all processes from the [`Drop`]
/// thread.
///
/// The default implementation does nothing.
fn stop(&mut self) {}
/// Start the evaluation of the i^th subproblem at the given point.
///
/// The results of the evaluation should be passed to the provided channel.
/// In order to work correctly, the results must contain (an upper bound on)
/// the objective value at $y$ as well as at least one subgradient centered
/// at $y$ eventually.
fn evaluate<I: Send + Copy + 'static>(
&mut self,
i: usize,
y: Arc<DVector>,
index: I,
tx: ResultSender<I, Self::Primal, Self::Err>,
) -> Result<(), Self::Err>;
/// Called to update the problem.
///
/// This method is called regularly by the solver. The problem should send problem update
/// information (e.g. adding new variables) to the provided channel.
///
/// The updates might be generated asynchronously.
///
/// The default implementation does nothing.
fn update<I, U>(
&mut self,
_state: &U,
_index: I,
_tx: UpdateSender<I, Self::Primal, Self::Err>,
) -> Result<(), Self::Err>
where
U: UpdateState<Self::Primal>,
{
Ok(())
}
}