honegumi.html

docs/honegumi.html

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                 <h2>Honegumi</h2>
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             <label class="row-label">Objective:</label>
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             <input type="radio" id="objective-Single" name="objective" value="Single"  checked py-click="update_text">
             <label class="row-option" for="objective-Single">Single</label>
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             <input type="radio" id="objective-Multi" name="objective" value="Multi"  py-click="update_text">
             <label class="row-option" for="objective-Multi">Multi</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose between <a href='/docs/curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo.html'>single and multi-objective optimization</a> based on your project needs. Single objective optimization targets one primary goal (e.g. maximize the strength of a material), while multi-objective optimization considers several objectives simultaneously (e.g. maximize the strength of a material while minimizing synthesis cost). Select the option that best aligns with your optimization goals and problem complexity.</span>
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             <label class="row-label">Model Type:</label>
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             <input type="radio" id="model-Default" name="model" value="Default"  checked py-click="update_text">
             <label class="row-option" for="model-Default">Default</label>
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             <input type="radio" id="model-Custom" name="model" value="Custom"  py-click="update_text">
             <label class="row-option" for="model-Custom">Custom</label>
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             <input type="radio" id="model-Fully Bayesian" name="model" value="Fully Bayesian"  py-click="update_text">
             <label class="row-option" for="model-Fully Bayesian">Fully Bayesian</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose between three surrogate model implementations: Default uses a standard Gaussian process (GP), Custom enables user-defined acquisition functions and hyperparameters, and <a href='/docs/curriculum/concepts/freq-vs-bayes/freq-vs-bayes.html'>Fully Bayesian</a> implements MCMC estimation of GP parameters. The Default option provides a robust baseline performance, Custom allows advanced users to tailor the optimization process, while Fully Bayesian offers deeper uncertainty exploration at higher computational cost. Consider your optimization needs and computational resources when selecting this option.</span>
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             <label class="row-label">Task:</label>
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             <input type="radio" id="task-Single" name="task" value="Single"  checked py-click="update_text">
             <label class="row-option" for="task-Single">Single</label>
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             <input type="radio" id="task-Multi" name="task" value="Multi"  py-click="update_text">
             <label class="row-option" for="task-Multi">Multi</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose between <a href='/docs/curriculum/concepts/single-vs-multi-task/single-vs-multi-task.html'>single and multi-task optimization</a> based on your experimental setup. Single-task optimization focuses on one specific task, while multi-task optimization leverages data from multiple related tasks simultaneously (e.g. optimizing similar manufacturing processes across different production sites). Multi-task optimization can improve efficiency by sharing information between tasks but requires related task structures. Consider whether your tasks share underlying similarities when making this selection.</span>
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             <label class="row-label">Categorical Parameter:</label>
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             <input type="radio" id="categorical-False" name="categorical" value="False"  checked py-click="update_text">
             <label class="row-option" for="categorical-False">False</label>
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             <input type="radio" id="categorical-True" name="categorical" value="True"  py-click="update_text">
             <label class="row-option" for="categorical-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to include a categorical variable in the optimization process (e.g. dark or milk chocolate chips in a cookie recipe). Including categorical variables allows choice parameters and their interaction with continuous variables to be optimized. Note that adding categorical variables can create discontinuities in the search space that are difficult to optimize over. Consider the value of adding categorical variables to the optimization task when selecting this option.</span>
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             <label class="row-label">Sum Constraint:</label>
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             <input type="radio" id="sum_constraint-False" name="sum_constraint" value="False"  checked py-click="update_text">
             <label class="row-option" for="sum_constraint-False">False</label>
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             <input type="radio" id="sum_constraint-True" name="sum_constraint" value="True"  py-click="update_text">
             <label class="row-option" for="sum_constraint-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to apply a sum constraint over two or more optimization variables (e.g. ensuring total allocation remains within available budget). This constraint focusses generated optimization trials on feasible candidates at the cost of flexibility. Consider whether such a constraint reflects the reality of variable interactions when selecting this option.</span>
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         <div class="row">
             <label class="row-label">Order Constraint:</label>
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             <input type="radio" id="order_constraint-False" name="order_constraint" value="False"  checked py-click="update_text">
             <label class="row-option" for="order_constraint-False">False</label>
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             <input type="radio" id="order_constraint-True" name="order_constraint" value="True"  py-click="update_text">
             <label class="row-option" for="order_constraint-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to implement an order constraint over two or more optimization variables (e.g. ensuring certain tasks precede others). This constraint focusses generated optimization trials on variable combinations that follow a specific order. Excluding the constraint offers flexibility in variable arrangements but may neglect important task sequencing or value inequality considerations. Consider whether such a constraint reflects the reality of variable interactions when selecting this option.</span>
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             <label class="row-label">Linear Constraint:</label>
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             <input type="radio" id="linear_constraint-False" name="linear_constraint" value="False"  checked py-click="update_text">
             <label class="row-option" for="linear_constraint-False">False</label>
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             <input type="radio" id="linear_constraint-True" name="linear_constraint" value="True"  py-click="update_text">
             <label class="row-option" for="linear_constraint-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to implement a linear constraint over two or more optimization variables such that the linear combination of parameter values adheres to an inequality (e.g. 0.2*x_1 + x_2 < 0.1). This constraint focusses generated optimization trials on variable combinations that follow an enforced rule at the cost of flexibility. Consider whether such a constraint reflects the reality of variable interactions when selecting this option.</span>
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             <label class="row-label">Composition Constraint:</label>
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             <input type="radio" id="composition_constraint-False" name="composition_constraint" value="False"  checked py-click="update_text">
             <label class="row-option" for="composition_constraint-False">False</label>
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             <input type="radio" id="composition_constraint-True" name="composition_constraint" value="True"  py-click="update_text">
             <label class="row-option" for="composition_constraint-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to include a composition constraint over two or more optimization variables such that their sum does not exceed a specified total (e.g. ensuring the mole fractions of elements in a composition sum to one). This constraint is particularly relevant to fabrication-related tasks where the quantities of components must sum to a total. Consider whether such a constraint reflects the reality of variable interactions when selecting this option.</span>
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             <label class="row-label">Custom Threshold:</label>
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             <input type="radio" id="custom_threshold-False" name="custom_threshold" value="False"  checked py-click="update_text">
             <label class="row-option" for="custom_threshold-False">False</label>
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             <input type="radio" id="custom_threshold-True" name="custom_threshold" value="True"  py-click="update_text">
             <label class="row-option" for="custom_threshold-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to apply custom thresholds to objectives in a multi-objective optimization problem (e.g. a minimum acceptable strength requirement for a material). Setting a threshold on an objective guides the optimization algorithm to prioritize solutions that meet or exceed these criteria. Excluding thresholds enables greater exploration of the design space, but may produce sub-optimal solutions. Consider whether threshold values reflect the reality or expectations of your optimization task when selection this option.</span>
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             <label class="row-label">Existing Data:</label>
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             <input type="radio" id="existing_data-False" name="existing_data" value="False"  checked py-click="update_text">
             <label class="row-option" for="existing_data-False">False</label>
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             <input type="radio" id="existing_data-True" name="existing_data" value="True"  py-click="update_text">
             <label class="row-option" for="existing_data-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to fit the surrogate model to previous data before starting the optimization process. Including historical data may give your model a better starting place and potentially speed up convergence. Conversely, excluding existing data means starting the optimization from scratch, which might be preferred in scenarios where historical data could introduce bias or noise into the optimization process. Consider the relevance and reliability of your existing data when making your selection.</span>
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             <label class="row-label">Synchrony:</label>
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             <input type="radio" id="synchrony-Single" name="synchrony" value="Single"  checked py-click="update_text">
             <label class="row-option" for="synchrony-Single">Single</label>
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             <input type="radio" id="synchrony-Batch" name="synchrony" value="Batch"  py-click="update_text">
             <label class="row-option" for="synchrony-Batch">Batch</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to perform <a href='curriculum/concepts/batch/single-vs-batch.md'>single or batch evaluations</a> for your Bayesian optimization campaign. Single evaluations analyze one candidate solution at a time, offering precise control and adaptability after each trial at the expense of more compute time. Batch evaluations, however, process several solutions in parallel, significantly reducing the number of optimization cycles but potentially diluting the specificity of adjustments. Batch evaluation is helpful in scenarios where it is advantageous to test several solutions simultaneously. Consider the nature of your evaluation tool when selecting between the two options.</span>
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             <label class="row-label">Visualize Results:</label>
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             <input type="radio" id="visualize-False" name="visualize" value="False"  checked py-click="update_text">
             <label class="row-option" for="visualize-False">False</label>
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             <input type="radio" id="visualize-True" name="visualize" value="True"  py-click="update_text">
             <label class="row-option" for="visualize-True">True</label>
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             <div class="tooltip-icon" style="font-weight: bold;">&#9432;
                 <span class="tooltip-text arrow">Choose whether to include visualization tools for tracking optimization progress. The default visualizations display key performance metrics like optimization traces and model uncertainty (e.g. objective value convergence over time). Including visualizations helps monitor optimization progress and identify potential issues, but may add minor computational overhead. Consider whether real-time performance tracking would benefit your optimization workflow when selecting this option.</span>
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             <p id="preamble"></p>