Adds continuous objectives to the documentation

Author: miguelgondu

docs/protein-optimization/_toc.yml

@@ -23,6 +23,7 @@ parts:
       - file: using_poli/objective_repository/all_objectives.md
       - file: using_poli/objective_repository/white_noise.md
       - file: using_poli/objective_repository/aloha.md
+      - file: using_poli/objective_repository/toy_continuous_problems.md
       - file: using_poli/objective_repository/rdkit_qed.md
       - file: using_poli/objective_repository/rdkit_logp.md
       - file: using_poli/objective_repository/dockstring.md

docs/protein-optimization/index.md

@@ -29,6 +29,12 @@ White noise drawn from a unit Gaussian
 A toy example about optimizing 5-letter words to spell "ALOHA"
 :::
 
+:::{grid-item-card} Toy continuous problems
+:link: ./using_poli/objective_repository/toy_continuous_problems.html
+:columns: 6
+The usual benchmark functions for continuous optimization (e.g. `easom`, or `ackley_function_01`)
+:::
+
 ::::
 
 ### Small molecules

docs/protein-optimization/references.bib

@@ -70,3 +70,11 @@ @article{GarciaOrtegon:dockstring:2022
   language = {en}
 }
 
+@MISC{Al-Roomi:continuous_objective_benchmarks:2015,
+author = {Ali R. Al-Roomi},
+title = {{Unconstrained Single-Objective Benchmark Functions Repository}},
+year = {2015},
+address = {Halifax, Nova Scotia, Canada},
+institution = {Dalhousie University, Electrical and Computer Engineering},
+url = {https://www.al-roomi.org/benchmarks/unconstrained}
+}

docs/protein-optimization/using_poli/objective_repository/all_objectives.md

@@ -20,6 +20,12 @@ White noise drawn from a unit Gaussian
 A toy example about optimizing 5-letter words to spell "ALOHA"
 :::
 
+:::{grid-item-card} Toy continuous problems
+:link: ./toy_continuous_problems.html
+:columns: 6
+The usual benchmark functions for continuous optimization (e.g. `easom`, or `ackley_function_01`)
+:::
+
 ::::
 
 ## Small molecules

docs/protein-optimization/using_poli/objective_repository/toy_continuous_problems.md

@@ -0,0 +1,61 @@
+# Toy continuous objective functions
+
+![Type of objective function: continuous](https://img.shields.io/badge/Input_type-continuous-red)
+![Environment to run this objective function: poli base](https://img.shields.io/badge/Environment-poli____base-teal
+)
+
+## About
+
+These are the usual objective function people use to test continuous optimizers {cite:p}`Al-Roomi:continuous_objective_benchmarks:2015`. In particular, we include:
+
+- For $n$-dimensional optimization:
+    - [`ackley_function_01`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/227-ackley-s-function-no-1-or-ackley-s-path-function),
+    - [`alpine_01`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/162-alpine-function-no-1),
+    - [`alpine_02`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/163-alpine-function-no-2),
+    - [`bent_cigar`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/164-bent-cigar-function),
+    - [`brown`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/241-brown-s-function),
+    - [`chung_reynolds`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/165-chung-reynolds-function),
+    - [`cosine_mixture`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/166-cosine-mixture-function),
+    - [`deb_01`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/231-deb-s-function-no-01),
+    - [`deb_02`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/232-deb-s-function-no-02),
+    - [`deflected_corrugated_spring`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/238-deflected-corrugated-spring-function),
+    - `shifted_sphere` ($\sum_{d=1}^n (x_d - 1)^2$),
+    - [`egg_holder`](https://www.al-roomi.org/benchmarks/unconstrained/n-dimensions/187-egg-holder-function),
+- For specific dimensions:
+    - [`easom`](https://www.al-roomi.org/benchmarks/unconstrained/2-dimensions/22-easom-s-function), which is only available in 2 dimensions.
+    - [`cross_in_tray`](https://www.al-roomi.org/benchmarks/unconstrained/2-dimensions/44-cross-in-tray-function), which is only available in 2 dimensions.
+
+:::{warning}
+
+Some of the signs might be flipped, since we are usually interested in maximization. Some of the constants inside might also be changed for scale. [For the definitive version we used, check the implementation](https://github.com/MachineLearningLifeScience/poli/blob/dev/src/poli/objective_repository/toy_continuous_problem/definitions.py).
+
+:::
+
+## Prerequisites
+
+None, this function should always run out-of-the-box
+
+## How to run
+
+```python
+import numpy as np
+from poli import objective_factory
+
+# Choose a function name and number of dimensions
+function_name = "ackley_function_01"
+n_dimensions = 2  # it's 2 by default.
+
+# How to create
+problem_info, f, x0, y0, run_info = objective_factory.create(
+    name="toy_continuous_problem",
+    function_name=function_name,
+    n_dimensions=n_dimensions,  # For some, this can be arbitrary.
+)
+
+
+# Example input:
+x = np.array([[0.0, 0.0]])  # must be of shape [b, n_dimensions], in this case [1, 2].
+
+# Querying:
+print(f(x))  # Should be [[0.0]] in this example
+```