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‎_sources/using_poli_baselines/alebo.md

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# Adaptive Linear Embedding Bayesian Optimization (ALEBO)
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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[![Ax (py3.10 in conda)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml)
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## About
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/fb7d3b6f48c58d05c114cab4ff45b8f5c02428c5/src/poli_baselines/core/utils/ax/environment.ax.yml#L1).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/core/utils/ax/environment.ax.yml
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conda activate poli__ax
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```
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:::
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```python

‎_sources/using_poli_baselines/baxus.md

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# Adaptively expanding subspaces (BAxUS)
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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[![BAxUS (py3.10 in conda)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-baxus.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-baxus.yml)
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## About
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/main/src/poli_baselines/solvers/bayesian_optimization/baxus/environment.baxus.yml).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/solvers/bayesian_optimization/baxus/environment.baxus.yml
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conda activate poli__baxus
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```
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‎_sources/using_poli_baselines/bayesian_optimization.md

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# Bayesian Optimization
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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## About
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‎_sources/using_poli_baselines/bounce.md

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## How to run
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:::{warning}
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This solver runs in a different conda environment than base.
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/main/src/poli_baselines/solvers/bayesian_optimization/bounce/environment.bounce.yml).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/solvers/bayesian_optimization/bounce/environment.bounce.yml
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conda activate poli__bounce
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```
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:::
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```python
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import numpy as np

‎_sources/using_poli_baselines/cma_es.md

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# CMA-ES
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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## About
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‎_sources/using_poli_baselines/hvarfners_vanilla_bo.md

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# Hvarfner's Vanilla Bayesian Optimization
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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[![Ax (py3.10 in conda)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml)
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## About
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/fb7d3b6f48c58d05c114cab4ff45b8f5c02428c5/src/poli_baselines/core/utils/ax/environment.ax.yml#L1).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/core/utils/ax/environment.ax.yml
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conda activate poli__ax
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```
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```python

‎_sources/using_poli_baselines/line_bayesian_optimization.md

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# Line Bayesian Optimization
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## About
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‎_sources/using_poli_baselines/probrep.md

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## How to run
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:::{warning}
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This solver runs in a different conda environment than base.
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/main/src/poli_baselines/solvers/bayesian_optimization/pr/environment.pr.yml).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/solvers/bayesian_optimization/pr/environment.pr.yml
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conda activate poli__pr
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```
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:::
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```python
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import numpy as np

‎_sources/using_poli_baselines/saasbo.md

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# Sparse Axis-Aligned Subspaces Bayesian Optimization (SAASBO)
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![Type of optimizer algorithm: continuous inputs](https://img.shields.io/badge/Type-continuous_inputs-red)
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[![Ax (py3.10 in conda)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml)
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## About
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You can find a [conda environment where this solver can run here](https://github.com/MachineLearningLifeScience/poli-baselines/blob/fb7d3b6f48c58d05c114cab4ff45b8f5c02428c5/src/poli_baselines/core/utils/ax/environment.ax.yml#L1).
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If you have cloned `poli-baselines` locally:
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```bash
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conda env create --file src/poli_baselines/core/utils/ax/environment.ax.yml
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conda activate poli__ax
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```
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```python

‎_sources/using_poli_baselines/turbo.md

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# Trust Region Bayesian Optimization (Turbo)
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[![Base (python 3.9 in conda)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml)
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## About

‎searchindex.js

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‎using_poli_baselines/alebo.html

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<section class="tex2jax_ignore mathjax_ignore" id="adaptive-linear-embedding-bayesian-optimization-alebo">
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<h1>Adaptive Linear Embedding Bayesian Optimization (ALEBO)<a class="headerlink" href="#adaptive-linear-embedding-bayesian-optimization-alebo" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" />
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" />
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<a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml"><img alt="Ax (py3.10 in conda)" src="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml/badge.svg" /></a></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p class="admonition-title">Warning</p>
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<p>This solver runs in a different conda environment than base.</p>
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<p>You can find a <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/blob/fb7d3b6f48c58d05c114cab4ff45b8f5c02428c5/src/poli_baselines/core/utils/ax/environment.ax.yml#L1">conda environment where this solver can run here</a>.</p>
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<p>If you have cloned <code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code> locally:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>--file<span class="w"> </span>src/poli_baselines/core/utils/ax/environment.ax.yml
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conda<span class="w"> </span>activate<span class="w"> </span>poli__ax
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</pre></div>
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</div>
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</div>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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‎using_poli_baselines/baxus.html

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<section class="tex2jax_ignore mathjax_ignore" id="adaptively-expanding-subspaces-baxus">
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<h1>Adaptively expanding subspaces (BAxUS)<a class="headerlink" href="#adaptively-expanding-subspaces-baxus" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" />
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" />
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<a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-baxus.yml"><img alt="BAxUS (py3.10 in conda)" src="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-baxus.yml/badge.svg" /></a></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p class="admonition-title">Warning</p>
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<p>This solver runs in a different conda environment than base.</p>
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<p>You can find a <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/blob/main/src/poli_baselines/solvers/bayesian_optimization/baxus/environment.baxus.yml">conda environment where this solver can run here</a>.</p>
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<p>If you have cloned <code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code> locally:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>--file<span class="w"> </span>src/poli_baselines/solvers/bayesian_optimization/baxus/environment.baxus.yml
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conda<span class="w"> </span>activate<span class="w"> </span>poli__baxus
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</pre></div>
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</div>
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</div>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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‎using_poli_baselines/bayesian_optimization.html

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<section class="tex2jax_ignore mathjax_ignore" id="bayesian-optimization">
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<h1>Bayesian Optimization<a class="headerlink" href="#bayesian-optimization" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" /></p>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" /></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p>Bayesian Optimization is a sample-efficient black box optimization algorithm which uses an uncertainty-aware approximation <span class="math notranslate nohighlight">\(\tilde{f}(\boldsymbol{x})\)</span> of the objective function <span class="math notranslate nohighlight">\(f\)</span>. This surrogate model <span class="math notranslate nohighlight">\(\tilde{f}\)</span> is usually a Gaussian Process, whose predictions and uncertainties are used to build an <em>acquisition function</em> <span class="math notranslate nohighlight">\(\alpha(\boldsymbol{x})\)</span>. Optimizing <span class="math notranslate nohighlight">\(\alpha\)</span> renders points that are <em>likely</em> to perform well for <span class="math notranslate nohighlight">\(f\)</span>. By smartly including uncertainties in <span class="math notranslate nohighlight">\(\alpha\)</span>, Bayesian Optimization balances exploration and exploitation.</p>

‎using_poli_baselines/bounce.html

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</section>
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<section id="how-to-run">
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<h2>How to run<a class="headerlink" href="#how-to-run" title="Permalink to this heading">#</a></h2>
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<div class="admonition warning">
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<p class="admonition-title">Warning</p>
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<p>This solver runs in a different conda environment than base.</p>
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<p>You can find a <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/blob/main/src/poli_baselines/solvers/bayesian_optimization/bounce/environment.bounce.yml">conda environment where this solver can run here</a>.</p>
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<p>If you have cloned <code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code> locally:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>--file<span class="w"> </span>src/poli_baselines/solvers/bayesian_optimization/bounce/environment.bounce.yml
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conda<span class="w"> </span>activate<span class="w"> </span>poli__bounce
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</pre></div>
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</div>
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</div>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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‎using_poli_baselines/cma_es.html

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<section class="tex2jax_ignore mathjax_ignore" id="cma-es">
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<h1>CMA-ES<a class="headerlink" href="#cma-es" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" /></p>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" /></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p>Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES) maintains the mean <span class="math notranslate nohighlight">\(\boldsymbol{\mu}\)</span> and the covariance <span class="math notranslate nohighlight">\(\boldsymbol{\Sigma}\)</span> of a Normal distribution, updating it using a subset of the best-performing members at each iteration <span id="id1">[<a class="reference internal" href="../bibliography.html#id20" title="N. Hansen and A. Ostermeier. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In Proceedings of IEEE International Conference on Evolutionary Computation, volume, 312-317. 1996. doi:10.1109/ICEC.1996.542381.">Hansen and Ostermeier, 1996</a>]</span>.</p>

‎using_poli_baselines/hvarfners_vanilla_bo.html

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<section class="tex2jax_ignore mathjax_ignore" id="hvarfner-s-vanilla-bayesian-optimization">
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<h1>Hvarfner’s Vanilla Bayesian Optimization<a class="headerlink" href="#hvarfner-s-vanilla-bayesian-optimization" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" />
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" />
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<a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml"><img alt="Ax (py3.10 in conda)" src="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing-ax.yml/badge.svg" /></a></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p class="admonition-title">Warning</p>
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<p>This solver runs in a different conda environment than base.</p>
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<p>You can find a <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/blob/fb7d3b6f48c58d05c114cab4ff45b8f5c02428c5/src/poli_baselines/core/utils/ax/environment.ax.yml#L1">conda environment where this solver can run here</a>.</p>
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<p>If you have cloned <code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code> locally:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>--file<span class="w"> </span>src/poli_baselines/core/utils/ax/environment.ax.yml
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conda<span class="w"> </span>activate<span class="w"> </span>poli__ax
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</pre></div>
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</div>
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</div>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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‎using_poli_baselines/line_bayesian_optimization.html

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<section class="tex2jax_ignore mathjax_ignore" id="line-bayesian-optimization">
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<h1>Line Bayesian Optimization<a class="headerlink" href="#line-bayesian-optimization" title="Permalink to this heading">#</a></h1>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-cyan" /></p>
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<p><img alt="Type of optimizer algorithm: continuous inputs" src="https://img.shields.io/badge/Type-continuous_inputs-red" /></p>
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<section id="about">
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<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">#</a></h2>
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<p>Line Bayesian Optimization (LineBO) is a version of <a class="reference internal" href="bayesian_optimization.html"><span class="doc std std-doc">Bayesian Optimization</span></a> that restricts the optimization of the acquisition function to a single line in input space <span id="id1">[<a class="reference internal" href="../bibliography.html#id16" title="Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, and Andreas Krause. Adaptive and safe bayesian optimization in high dimensions via one-dimensional subspaces. In Proceedings of the 36th International Conference on Machine Learning, 3429–3438. PMLR, May 2019. URL: https://proceedings.mlr.press/v97/kirschner19a.html.">Kirschner <em>et al.</em>, 2019</a>]</span>. This line can either be selected at random, or can follow one of the coordinate directions.</p>

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