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// Copyright 2018-2024 argmin developers
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://apache.org/licenses/LICENSE-2.0> or the MIT license <LICENSE-MIT or
// http://opensource.org/licenses/MIT>, at your option. This file may not be
// copied, modified, or distributed except according to those terms.
//! # Beta update methods for [`NonlinearConjugateGradient`](`crate::solver::conjugategradient::NonlinearConjugateGradient`)
//!
//! These methods define the update procedure for
//! [`NonlinearConjugateGradient`](`crate::solver::conjugategradient::NonlinearConjugateGradient`).
//! They are based on the [`NLCGBetaUpdate`] trait which enables users to implement their own beta
//! update methods.
//!
//! # Reference
//!
//! \[0\] Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization.
//! Springer. ISBN 0-387-30303-0.
use crate::core::ArgminFloat;
use argmin_math::{ArgminDot, ArgminL2Norm, ArgminSub};
#[cfg(feature = "serde1")]
use serde::{Deserialize, Serialize};
/// Interface for beta update methods ([`NonlinearConjugateGradient`](`crate::solver::conjugategradient::NonlinearConjugateGradient`))
///
/// # Example
///
/// ```
/// # use argmin::core::{ArgminFloat, NLCGBetaUpdate};
/// #[cfg(feature = "serde1")]
/// use serde::{Deserialize, Serialize};
///
/// #[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
/// struct MyBetaMethod {}
///
/// impl<G, P, F> NLCGBetaUpdate<G, P, F> for MyBetaMethod
/// where
/// F: ArgminFloat,
/// {
/// fn update(&self, dfk: &G, dfk1: &G, p_k: &P) -> F {
/// // Compute updated beta
/// # F::nan()
/// }
/// }
/// ```
pub trait NLCGBetaUpdate<G, P, F> {
/// Update beta.
///
/// # Parameters
///
/// * `\nabla f_k`
/// * `\nabla f_{k+1}`
/// * `p_k`
fn update(&self, nabla_f_k: &G, nabla_f_k_p_1: &G, p_k: &P) -> F;
}
/// Fletcher and Reeves (FR) method
///
/// Formula: `<\nabla f_{k+1}, \nabla f_{k+1}> / <\nabla f_k, \nabla f_k>`
#[derive(Default, Copy, Clone, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct FletcherReeves {}
impl FletcherReeves {
/// Construct a new instance of `FletcherReeves`.
///
/// # Example
///
/// ```
/// # use argmin::solver::conjugategradient::beta::FletcherReeves;
/// let beta_method = FletcherReeves::new();
/// ```
pub fn new() -> Self {
FletcherReeves {}
}
}
impl<G, P, F> NLCGBetaUpdate<G, P, F> for FletcherReeves
where
G: ArgminDot<G, F>,
F: ArgminFloat,
{
/// Update beta using the Fletcher-Reeves method.
///
/// Formula: `<\nabla f_{k+1}, \nabla f_{k+1}> / <\nabla f_k, \nabla f_k>`
///
/// # Example
///
/// ```
/// # extern crate approx;
/// # use approx::assert_relative_eq;
/// # use argmin::solver::conjugategradient::beta::{NLCGBetaUpdate, FletcherReeves};
/// # let dfk = vec![1f64, 2.0];
/// # let dfk1 = vec![3f64, 4.0];
/// let beta_method = FletcherReeves::new();
/// let beta: f64 = beta_method.update(&dfk, &dfk1, &());
/// # assert_relative_eq!(beta, 5.0, epsilon = f64::EPSILON);
/// ```
fn update(&self, dfk: &G, dfk1: &G, _pk: &P) -> F {
dfk1.dot(dfk1) / dfk.dot(dfk)
}
}
/// Polak and Ribiere (PR) method
#[derive(Default, Copy, Clone, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct PolakRibiere {}
impl PolakRibiere {
/// Construct a new instance of `PolakRibiere`.
///
/// # Example
///
/// ```
/// # use argmin::solver::conjugategradient::beta::PolakRibiere;
/// let beta_method = PolakRibiere::new();
/// ```
pub fn new() -> Self {
PolakRibiere {}
}
}
impl<G, P, F> NLCGBetaUpdate<G, P, F> for PolakRibiere
where
G: ArgminDot<G, F> + ArgminSub<G, G> + ArgminL2Norm<F>,
F: ArgminFloat,
{
/// Update beta using the Polak-Ribiere method.
///
/// Formula: `<\nabla f_{k+1}, (\nabla f_{k+1} - \nabla f_k)> / ||\nabla f_k||^2`
///
/// # Example
///
/// ```
/// # extern crate approx;
/// # use approx::assert_relative_eq;
/// # use argmin::solver::conjugategradient::beta::{NLCGBetaUpdate, PolakRibiere};
/// # let dfk = vec![1f64, 2.0];
/// # let dfk1 = vec![3f64, 4.0];
/// let beta_method = PolakRibiere::new();
/// let beta = beta_method.update(&dfk, &dfk1, &());
/// # assert_relative_eq!(beta, 14.0/5.0, epsilon = f64::EPSILON);
/// ```
fn update(&self, dfk: &G, dfk1: &G, _pk: &P) -> F {
let dfk_norm_sq = dfk.l2_norm().powi(2);
dfk1.dot(&dfk1.sub(dfk)) / dfk_norm_sq
}
}
/// Polak and Ribiere Plus (PR+) method
///
/// Formula: `max(0, <\nabla f_{k+1}, (\nabla f_{k+1} - \nabla f_k)> / ||\nabla f_k||^2)`
#[derive(Default, Copy, Clone, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct PolakRibierePlus {}
impl PolakRibierePlus {
/// Construct a new instance of `PolakRibierePlus`.
///
/// # Example
///
/// ```
/// # use argmin::solver::conjugategradient::beta::PolakRibierePlus;
/// let beta_method = PolakRibierePlus::new();
/// ```
pub fn new() -> Self {
PolakRibierePlus {}
}
}
impl<G, P, F> NLCGBetaUpdate<G, P, F> for PolakRibierePlus
where
G: ArgminDot<G, F> + ArgminSub<G, G> + ArgminL2Norm<F>,
F: ArgminFloat,
{
/// Update beta using the Polak-Ribiere+ (PR+) method.
///
/// Formula: `max(0, <\nabla f_{k+1}, (\nabla f_{k+1} - \nabla f_k)> / ||\nabla f_k||^2)`
///
/// # Example
///
/// ```
/// # extern crate approx;
/// # use approx::assert_relative_eq;
/// # use argmin::solver::conjugategradient::beta::{NLCGBetaUpdate, PolakRibierePlus};
/// # let dfk = vec![1f64, 2.0];
/// # let dfk1 = vec![3f64, 4.0];
/// let beta_method = PolakRibierePlus::new();
/// let beta = beta_method.update(&dfk, &dfk1, &());
/// # assert_relative_eq!(beta, 14.0/5.0, epsilon = f64::EPSILON);
/// #
/// # let dfk = vec![5f64, 6.0];
/// # let dfk1 = vec![3f64, 4.0];
/// # let beta_method = PolakRibierePlus::new();
/// # let beta = beta_method.update(&dfk, &dfk1, &());
/// # assert_relative_eq!(beta, 0.0, epsilon = f64::EPSILON);
/// ```
fn update(&self, dfk: &G, dfk1: &G, _pk: &P) -> F {
let dfk_norm_sq = dfk.l2_norm().powi(2);
let beta = dfk1.dot(&dfk1.sub(dfk)) / dfk_norm_sq;
float!(0.0).max(beta)
}
}
/// Hestenes and Stiefel (HS) method
///
/// Formula: `<\nabla f_{k+1}, (\nabla f_{k+1} - \nabla f_k)> / <(\nabla f_{k+1} - \nabla f_k), p_k>`
#[derive(Default, Copy, Clone, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct HestenesStiefel {}
impl HestenesStiefel {
/// Construct a new instance of `HestenesStiefel`.
///
/// # Example
///
/// ```
/// # use argmin::solver::conjugategradient::beta::HestenesStiefel;
/// let beta_method = HestenesStiefel::new();
/// ```
pub fn new() -> Self {
HestenesStiefel {}
}
}
impl<G, P, F> NLCGBetaUpdate<G, P, F> for HestenesStiefel
where
G: ArgminDot<G, F> + ArgminDot<P, F> + ArgminSub<G, G>,
F: ArgminFloat,
{
/// Update beta using the Hestenes-Stiefel method.
///
/// Formula: `<\nabla f_{k+1}, (\nabla f_{k+1} - \nabla f_k)> / <(\nabla f_{k+1} - \nabla f_k), p_k>`
///
/// # Example
///
/// ```
/// # extern crate approx;
/// # use approx::assert_relative_eq;
/// # use argmin::solver::conjugategradient::beta::{NLCGBetaUpdate, HestenesStiefel};
/// # let dfk = vec![1f64, 2.0];
/// # let dfk1 = vec![3f64, 4.0];
/// # let pk = vec![5f64, 6.0];
/// let beta_method = HestenesStiefel::new();
/// let beta: f64 = beta_method.update(&dfk, &dfk1, &pk);
/// # assert_relative_eq!(beta, 14.0/22.0, epsilon = f64::EPSILON);
/// ```
fn update(&self, dfk: &G, dfk1: &G, pk: &P) -> F {
let d = dfk1.sub(dfk);
dfk1.dot(&d) / d.dot(pk)
}
}
#[cfg(test)]
mod tests {
use super::*;
test_trait_impl!(fletcher_reeves, FletcherReeves);
test_trait_impl!(polak_ribiere, PolakRibiere);
test_trait_impl!(polak_ribiere_plus, PolakRibierePlus);
test_trait_impl!(hestenes_stiefel, HestenesStiefel);
}