Some simple multi-threaded optimizers.
You could do a random search like:
rust
use std::sync::Arc;
use simple_optimization::{random_search, Polling};
// Our evaluation function takes 3 `f64`s and no additional data `()`.
fn simple_function(list: &[f64; 3], _: Option<Arc::<()>>) -> f64 { list.iter().sum() }
let best = random_search!(
(0f64..10f64, 5u32..15u32, 10i16..20i16), // Value ranges.
simple_function, // Evaluation function.
None, // No additional evaluation data.
// Every `10ms` we print progress and exit early if `simple_function` return a value less than `19.`.
Some(Polling::new(true,Some(19.))),
None, // We don't specify the number of threads to run.
1000, // Take `1000` samples.
);
assert!(simple_function(&best, None) < 19.);
Which during execution will give an output like:
1000
500 (50.00%) 00:00:11 / 00:00:47 [25.600657363049734]
Representing:
<Total number of samples>
<Current number of samples> (<Percentage of samples taken>) <Time running> / <ETA> [<Current best value>]
Optimizer | Status ---|--- Random search|✅ Grid search|✅ Simulated annealing|✅ Bayesian optimization|WIP Gradient descent| See my note here Genetic algorithms| No plans Ant colony optimization| No plans Linear programming| No plans
I made this for my own use since the existing libraries I found felt awkward.