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| title = "argmin_testfunctions 0.2.0 and argmin-testfunctions-py v0.0.1 released" | ||
| description = "" | ||
| date = 2024-02-16T00:00:00+00:00 | ||
| updated = 2024-02-16T00:00:00+00:00 | ||
| draft = false | ||
| template = "blog/page.html" | ||
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| [taxonomies] | ||
| authors = ["Stefan Kroboth"] | ||
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| [extra] | ||
| +++ | ||
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| <b>argmin</b> is a Rust library which offers a range of numerical optimization methods and is a framework for | ||
| developing optimization algorithms. Details about the design and features of argmin can be found on | ||
| [the website](https://argmin-rs.org), | ||
| in the [book](https://argmin-rs.org/book), | ||
| on [Github](https://github.com/argmin-rs/argmin), | ||
| on [crates.io](https://crates.io/crates/argmin) and | ||
| on [lib.rs](https://lib.rs/crates/argmin). | ||
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| <b>argmin_testfunctions</b> is one of the Rust libraries in the argmin ecosystem and provides a wide range of | ||
| test functions for optimization problems (not just for argmin). For many years it has been sitting around in its repo without | ||
| getting much attention. | ||
| I decided to change that by pulling the code into the argmin monorepo, updating it to the current Rust edition | ||
| and started implementing new features. The main addition is the calculation of the derivatives and Hessians | ||
| for all cost functions. | ||
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| As input, the functions now take parameters either as `&[T]`, `&[T; 2]`, or `&[T; N]` where `N` is a const generic and | ||
| `T: Float` (`f64` and `f32`). | ||
| Functions which only accept two dimensional input accept `&[T; 2]` and return `T` (test function), `[T; 2]` (derivative), or `[[T; 2]; 2]` | ||
| (Hessian). | ||
| An example for this is the Himmelblau test function: | ||
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| ```rust | ||
| use argmin_testfunctions::{himmelblau, himmelblau_derivative, himmelblau_hessian}; | ||
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| let p: [f64; 2] = [0.1, 0.2]; | ||
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| let c: f64 = himmelblau(&p); | ||
| let d: [f64; 2] = himmelblau_derivative(&p); | ||
| let h: [[f64; 2]; 2] = himmelblau_hessian(&p); | ||
| ``` | ||
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| Functions which accept an arbitrary number of parameters come in two forms. One variant accepts `&[T]` and returns | ||
| `T` (test function), `Vec<T>` (derivative), or `Vec<Vec<T>>` (Hessian), for instance: | ||
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| ```rust | ||
| use argmin_testfunctions::{rosenbrock, rosenbrock_derivative, rosenbrock_hessian}; | ||
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| let p = [0.1, 0.2, 0.3]; | ||
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| let c: f64 = rosenbrock(&p); | ||
| let d: Vec<f64> = rosenbrock_derivative(&p); | ||
| let h: Vec<Vec<f64>> = rosenbrock_hessian(&p); | ||
| ``` | ||
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| This obviously allocates `Vec`s and as such isn't the most efficient. If the number of parameters are known | ||
| at compile-time, one can use the const generics version of the functions which take `&[T; N]` and return | ||
| `[T; N]` (derivative) or `[[T; N]; N]` (Hessian): | ||
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| ```rust | ||
| use argmin_testfunctions::{ | ||
| rosenbrock, rosenbrock_derivative_const, rosenbrock_hessian_const | ||
| }; | ||
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| let p: [f64; 3] = [0.1, 0.2, 0.3]; | ||
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| let c: f64 = rosenbrock(&p); | ||
| let d: [f64; 3] = rosenbrock_derivative_const(&p); | ||
| let h: [[f64; 3]; 3] = rosenbrock_hessian_const(&p); | ||
| ``` | ||
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| This does not allocate and hence is generally faster. | ||
| The Rosenbrock test function also has additional optional parameters `a` and `b` which can be adjusted to one's needs: | ||
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| ```rust | ||
| use argmin_testfunctions::{ | ||
| rosenbrock_ab, rosenbrock_ab_derivative, rosenbrock_ab_derivative_const, | ||
| rosenbrock_ab_hessian, rosenbrock_ab_hessian_const | ||
| }; | ||
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| let p = [0.1, 0.2, 0.3]; | ||
| let a = 5.0; | ||
| let b = 200.0; | ||
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| let c: f64 = rosenbrock_ab(&p, a, b); | ||
| let d: Vec<f64> = rosenbrock_ab_derivative(&p, a, b); | ||
| let h: Vec<Vec<f64>> = rosenbrock_ab_hessian(&p, a, b); | ||
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| // There are also const generics versions | ||
| let d: [f64; 3] = rosenbrock_ab_derivative_const(&p, a, b); | ||
| let h: [[f64; 3]; 3] = rosenbrock_ab_hessian_const(&p, a, b); | ||
| ``` | ||
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| Similar functions exist for `ackley` (`ackley_abc`) and `rastrigin` (`rastrigin_a`). | ||
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| ## Python interface | ||
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| The new Python module `argmin-testfunctions-py` makes the library availably in Python as well. | ||
| It offers the same functions, apart from the const generics versions, which are missing. | ||
| Optional parameters such as `a` and `b` of the Rosenbrock test function are optional | ||
| arguments in Python: | ||
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| ```python | ||
| from argmin_testfunctions_py import * | ||
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| c = rosenbrock([0.1, 0.2, 0.3, 0.4], a = 5.0, b = 200.0) | ||
| g = rosenbrock_derivative([0.1, 0.2, 0.3, 0.4], a = 5.0, b = 200.0) | ||
| h = rosenbrock_hessian([0.1, 0.2, 0.3, 0.4], a = 5.0, b = 200.0) | ||
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| # When omitted, they will default to a = 1.0 and b = 100.0 | ||
| c = rosenbrock([0.1, 0.2, 0.3, 0.4]) | ||
| g = rosenbrock_derivative([0.1, 0.2, 0.3, 0.4]) | ||
| h = rosenbrock_hessian([0.1, 0.2, 0.3, 0.4]) | ||
| ``` | ||
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| This module is still a bit experimental, which is expressed with the version number 0.0.1. | ||
| Eventually the version of the Python module may end up in lockstep with `argmin_testfunctions`' version number. | ||
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| It can be found on [PyPI](https://pypi.org/project/argmin-testfunctions-py/) and installed via | ||
| `pip install argmin-testfunctions-py`. | ||
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| ## Tests and Benchmarks | ||
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| All test functions are tested against known points and the derivative and Hessian calculations | ||
| are tested against finite differences (using [finitediff](https://crates.io/crates/finitediff)). | ||
| Multiple parameter vectors are evaluated using [proptest](https://crates.io/crates/proptest). | ||
| As usual, tests can be executed with `cargo test`. | ||
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| The benchmarks now use [criterion.rs](https://crates.io/crates/criterion). `cargo bench` will benchmark all | ||
| functions and produce a report in `target/criterion/report/index.html`. | ||
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| ## Contributing | ||
|
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| This library's usefulness is directly proportional to the number of test functions it provides. | ||
| Therefore all contributions to this library are highly welcome. Interested people should have a look | ||
| at [this issue](https://github.com/argmin-rs/argmin/issues/450) which provides inspiration for what | ||
| to implement as well as a couple of hints on how to implement it such that it best fits into the library. | ||
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| While I believe that most implementations are quite efficient, I am sure that there are some which could | ||
| be improved. If anyone wants to get the last bit of performance out of this, feel free to open a PR. | ||
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| Ideally this library would also print the formulas in the docs | ||
| [as actual formulas](https://github.com/argmin-rs/argmin/issues/418) and having | ||
| [visualizations of the test functions](https://github.com/argmin-rs/argmin/issues/451) in the docs wouldn't hurt either. | ||
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| ## Conclusion | ||
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| With this release the `argmin_testfunctions` crate finally got derivative and Hessian calculations for | ||
| all test functions. | ||
| Thanks to `argmin-testfunctions-py` the functionality is now also available in Python. | ||
| Hopefully this crate will be of use to some. If you find bugs or have suggestions or feedback, feel free to get | ||
| in touch, either via Github or the Discord. | ||
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| Details on the individual test functions can be found in the documentation, either for the | ||
| [latest release](https://docs.rs/argmin_testfunctions) or | ||
| [the current main branch](https://argmin-rs.github.io/argmin/argmin_testfunctions/index.html). | ||
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| ## Discord server | ||
|
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| If you're interested you're invited to join the [Discord](https://discord.gg/fYB8AwxxMW)! | ||
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| <br> | ||
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| </p> |
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