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Merge branch 'release/0.15.0'
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@lucidfrontier45
lucidfrontier45 committed Aug 25, 2024
2 parents ce0592a + 5cb121f commit fdefe85
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Showing 18 changed files with 190 additions and 162 deletions.
2 changes: 1 addition & 1 deletion Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ categories = ["algorithms"]
repository = "https://github.com/lucidfrontier45/localsearch"
license-file = "LICENSE"
readme = "README.md"
version = "0.14.0"
version = "0.15.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -6,7 +6,7 @@ Rust library for local search optimization
All of the algorithms are parallelized with Rayon.

1. Hill Climbing.
2. Tabu Search.
2. Tabu Search. To use this optimizer you also need to implement your problem specific tabu list.
3. Simulated Annealing
4. Epsilon Greedy Search, a variant of Hill Climbing which accepts the trial solution with a constant probabilith even if the score of the trial solution is worse than the previous one.
5. Relative Annealing, a variant of Simulated Annealing which uses relative score diff to calculate transition probability.
Expand Down Expand Up @@ -110,7 +110,7 @@ fn main() {
pb.set_position(op.iter as u64);
};

let res = opt.run(&model, None, n_iter, time_limit, Some(&callback), ());
let res = opt.run(&model, None, n_iter, time_limit, Some(&callback));
pb.finish();
dbg!(res.unwrap());
}
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2 changes: 1 addition & 1 deletion examples/quadratic_model.rs
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Expand Up @@ -91,7 +91,7 @@ fn main() {
pb.set_position(op.iter as u64);
};

let res = opt.run(&model, None, n_iter, time_limit, Some(&callback), ());
let res = opt.run(&model, None, n_iter, time_limit, Some(&callback));
pb.finish();
dbg!(res.unwrap());
}
36 changes: 19 additions & 17 deletions examples/tsp_model.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -179,24 +179,32 @@ impl DequeTabuList {
}
}

impl TabuList for DequeTabuList {
type Item = (SolutionType, TransitionType);
impl Default for DequeTabuList {
fn default() -> Self {
Self::new(10)
}
}

fn contains(&self, item: &Self::Item) -> bool {
impl TabuList<TSPModel> for DequeTabuList {
fn contains(&self, item: &(SolutionType, TransitionType)) -> bool {
let (_, (_, inserted_edges)) = item;
inserted_edges
.iter()
.any(|edge| self.buff.iter().any(|e| *e == *edge))
}

fn append(&mut self, item: Self::Item) {
fn append(&mut self, item: (SolutionType, TransitionType)) {
let (_, (removed_edges, _)) = item;
for edge in removed_edges {
if self.buff.iter().all(|e| *e != edge) {
self.buff.append(edge);
}
}
}

fn set_size(&mut self, n: usize) {
self.buff = RingBuffer::new(n);
}
}

// The output is wrapped in a Result to allow matching on errors
Expand Down Expand Up @@ -261,14 +269,13 @@ fn main() {

println!("run hill climbing");
let optimizer = HillClimbingOptimizer::new(1000, 200);
let (final_solution, final_score, _) = optimizer
let (final_solution, final_score) = optimizer
.run(
&tsp_model,
initial_solution.clone(),
n_iter,
time_limit,
Some(&callback),
(),
)
.unwrap();
println!(
Expand All @@ -280,16 +287,14 @@ fn main() {
pb.reset();

println!("run tabu search");
let tabu_list = DequeTabuList::new(20);
let optimizer = TabuSearchOptimizer::new(patience, 200, 10);
let (final_solution, final_score, _) = optimizer
let optimizer = TabuSearchOptimizer::<TSPModel, DequeTabuList>::new(patience, 200, 10, 20);
let (final_solution, final_score) = optimizer
.run(
&tsp_model,
initial_solution.clone(),
n_iter,
time_limit,
Some(&callback),
tabu_list,
)
.unwrap();
println!(
Expand All @@ -301,15 +306,14 @@ fn main() {
pb.reset();

println!("run annealing");
let optimizer = SimulatedAnnealingOptimizer::new(patience, 200);
let (final_solution, final_score, _) = optimizer
let optimizer = SimulatedAnnealingOptimizer::new(patience, 200, 200.0, 50.0);
let (final_solution, final_score) = optimizer
.run(
&tsp_model,
initial_solution.clone(),
n_iter,
time_limit,
Some(&callback),
(200.0, 50.0),
)
.unwrap();
println!(
Expand All @@ -322,14 +326,13 @@ fn main() {

println!("run epsilon greedy");
let optimizer = EpsilonGreedyOptimizer::new(patience, 200, 10, 0.3);
let (final_solution, final_score, _) = optimizer
let (final_solution, final_score) = optimizer
.run(
&tsp_model,
initial_solution.clone(),
n_iter,
time_limit,
Some(&callback),
(),
)
.unwrap();
println!(
Expand All @@ -342,14 +345,13 @@ fn main() {

println!("run relative annealing");
let optimizer = RelativeAnnealingOptimizer::new(patience, 200, 10, 1e1);
let (final_solution, final_score, _) = optimizer
let (final_solution, final_score) = optimizer
.run(
&tsp_model,
initial_solution,
n_iter,
time_limit,
Some(&callback),
(),
)
.unwrap();
println!(
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16 changes: 4 additions & 12 deletions src/optim/base.rs
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Expand Up @@ -6,11 +6,6 @@ use crate::{callback::OptCallbackFn, Duration, OptModel};
/// Optimizer that implements local search algorithm
#[auto_impl(&, Box, Rc, Arc)]
pub trait LocalSearchOptimizer<M: OptModel> {
/// Extra input type
type ExtraIn;
/// Extra output type
type ExtraOut;

/// Start optimization
fn optimize<F>(
&self,
Expand All @@ -20,8 +15,7 @@ pub trait LocalSearchOptimizer<M: OptModel> {
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
M: OptModel,
F: OptCallbackFn<M::SolutionType, M::ScoreType>;
Expand All @@ -34,8 +28,7 @@ pub trait LocalSearchOptimizer<M: OptModel> {
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
extra_in: Self::ExtraIn,
) -> AnyResult<(M::SolutionType, M::ScoreType, Self::ExtraOut)>
) -> AnyResult<(M::SolutionType, M::ScoreType)>
where
M: OptModel,
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
Expand All @@ -51,18 +44,17 @@ pub trait LocalSearchOptimizer<M: OptModel> {
let (initial_solution, initial_score) =
model.preprocess_solution(initial_solution, initial_score)?;

let (solution, score, extra) = self.optimize(
let (solution, score) = self.optimize(
model,
initial_solution,
initial_score,
n_iter,
time_limit,
callback,
extra_in,
);

let (solution, score) = model.postprocess_solution(solution, score);
Ok((solution, score, extra))
Ok((solution, score))
}
}

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7 changes: 1 addition & 6 deletions src/optim/epsilon_greedy.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -39,8 +39,6 @@ impl EpsilonGreedyOptimizer {
}

impl<M: OptModel> LocalSearchOptimizer<M> for EpsilonGreedyOptimizer {
type ExtraIn = ();
type ExtraOut = ();
/// Start optimization
///
/// - `model` : the model to optimize
Expand All @@ -49,7 +47,6 @@ impl<M: OptModel> LocalSearchOptimizer<M> for EpsilonGreedyOptimizer {
/// - `n_iter`: maximum iterations
/// - `time_limit`: maximum iteration time
/// - `callback` : callback function that will be invoked at the end of each iteration
/// - `_extra_in` : not used
fn optimize<F>(
&self,
model: &M,
Expand All @@ -58,8 +55,7 @@ impl<M: OptModel> LocalSearchOptimizer<M> for EpsilonGreedyOptimizer {
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
_extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
M: OptModel + Sync + Send,
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
Expand All @@ -77,7 +73,6 @@ impl<M: OptModel> LocalSearchOptimizer<M> for EpsilonGreedyOptimizer {
n_iter,
time_limit,
callback,
_extra_in,
)
}
}
8 changes: 2 additions & 6 deletions src/optim/generic.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -55,8 +55,6 @@ where
FT: TransitionProbabilityFn<ST>,
M: OptModel<ScoreType = ST>,
{
type ExtraIn = ();
type ExtraOut = ();
/// Start optimization
///
/// - `model` : the model to optimize
Expand All @@ -65,7 +63,6 @@ where
/// - `n_iter`: maximum iterations
/// - `time_limit`: maximum iteration time
/// - `callback` : callback function that will be invoked at the end of each iteration
/// - `_extra_in` : not used
fn optimize<F>(
&self,
model: &M,
Expand All @@ -74,8 +71,7 @@ where
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
_extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
{
Expand Down Expand Up @@ -141,6 +137,6 @@ where
}

let best_solution = (*best_solution.borrow()).clone();
(best_solution, best_score, ())
(best_solution, best_score)
}
}
7 changes: 1 addition & 6 deletions src/optim/hill_climbing.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -19,9 +19,6 @@ impl HillClimbingOptimizer {
}

impl<M: OptModel> LocalSearchOptimizer<M> for HillClimbingOptimizer {
type ExtraIn = ();
type ExtraOut = ();

/// Start optimization
///
/// - `model` : the model to optimize
Expand All @@ -39,8 +36,7 @@ impl<M: OptModel> LocalSearchOptimizer<M> for HillClimbingOptimizer {
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
_extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
{
Expand All @@ -52,7 +48,6 @@ impl<M: OptModel> LocalSearchOptimizer<M> for HillClimbingOptimizer {
n_iter,
time_limit,
callback,
_extra_in,
)
}
}
7 changes: 1 addition & 6 deletions src/optim/logistic_annealing.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,6 @@ impl LogisticAnnealingOptimizer {
}

impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for LogisticAnnealingOptimizer {
type ExtraIn = ();
type ExtraOut = ();
/// Start optimization
///
/// - `model` : the model to optimize
Expand All @@ -54,7 +52,6 @@ impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for LogisticA
/// - `n_iter`: maximum iterations
/// - `time_limit`: maximum iteration time
/// - `callback` : callback function that will be invoked at the end of each iteration
/// - `_extra_in` : not used
fn optimize<F>(
&self,
model: &M,
Expand All @@ -63,8 +60,7 @@ impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for LogisticA
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
_extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
{
Expand All @@ -82,7 +78,6 @@ impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for LogisticA
n_iter,
time_limit,
callback,
_extra_in,
)
}
}
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7 changes: 1 addition & 6 deletions src/optim/relative_annealing.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -44,9 +44,6 @@ impl RelativeAnnealingOptimizer {
}

impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for RelativeAnnealingOptimizer {
type ExtraIn = ();
type ExtraOut = ();

/// Start optimization
///
/// - `model` : the model to optimize
Expand All @@ -64,8 +61,7 @@ impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for RelativeA
n_iter: usize,
time_limit: Duration,
callback: Option<&F>,
_extra_in: Self::ExtraIn,
) -> (M::SolutionType, M::ScoreType, Self::ExtraOut)
) -> (M::SolutionType, M::ScoreType)
where
F: OptCallbackFn<M::SolutionType, M::ScoreType>,
{
Expand All @@ -83,7 +79,6 @@ impl<M: OptModel<ScoreType = NotNan<f64>>> LocalSearchOptimizer<M> for RelativeA
n_iter,
time_limit,
callback,
_extra_in,
)
}
}
Expand Down
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