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feat: minor SEO and documentation improvements - #59
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| - **CMLON Support**: Compressed Monotonic LONs for cleaner landscape analysis | ||
| - **Rich Metrics**: Compute landscape metrics including funnel analysis and neutrality | ||
| - **Beautiful Visualizations**: 2D and 3D plots with support for animated GIFs | ||
| - **LON Construction**: Local Optima Networks as `igraph` graphs generated from sampled data with support for Compressed Monotonic LONs (CMLONs) representation. |
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Wording: "with support for Compressed Monotonic LONs (CMLONs) representation" reads awkwardly. Maybe: "…generated from sampled data, including Compressed Monotonic LONs (CMLONs)."
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Changed in 0dfc15d, it does sound better
| - **Rich Metrics**: Compute landscape metrics including funnel analysis and neutrality | ||
| - **Beautiful Visualizations**: 2D and 3D plots with support for animated GIFs | ||
| - **LON Construction**: Local Optima Networks as `igraph` graphs generated from sampled data with support for Compressed Monotonic LONs (CMLONs) representation. | ||
| - **Continuous landscape exploration**: Configurable Basin-Hopping algorithm for data sampling, with automatic step-size estimation and pararellization. |
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Typo: "pararellization" → "parallelization".
| - **Continuous landscape exploration**: Configurable Basin-Hopping algorithm for data sampling, with automatic step-size estimation and pararellization. | ||
| - **Discrete landscape exploration**: Iterated Local Search sampler, with parallelization, built-in problems (OneMax, NK Landscape, Number Partitioning) and classes to define custom problem instances. | ||
| - **Landscape metrics**: Funnel analysis, neutrality, global and sink strength, plus search performance (success rate, deviation from the known optimum). | ||
| - **Visualization**: Configurable 2D and 3D LON plots, and animated rotating GIFs. |
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Double space after the bullet dash (- **Visualization**).
| ### Custom Discrete Problems | ||
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| You can define your own discrete problem by subclassing `DiscreteProblem`. For bitstring problems, inherit from `BitstringProblem` instead; it provides `random_solution()`, `local_search()`, `perturb()`, and `solution_id()` out of the box. You only need to implement `evaluate()`. | ||
| You can define your own discrete problem by subclassing `DiscreteProblem`. You can also extend built-in problems - for bitstring problems, extend `BitstringProblem`, which it provides `random_solution()`, `local_search()`, `perturb()`, and `solution_id()` out of the box, so you only need to implement `evaluate()`. |
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Grammar: "extend BitstringProblem, which it provides…" → "which provides…".
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This PR makes the docs site and README better optimised for SEO and introduces small editorial changes. No library behaviour changes; the only code edits are to docstrings.
SEO and site metadata
docs/overrides/main.html: adds Open Graph and Twitter card meta tags with per-page title and description, plus an optionalgoogle-site-verificationmeta tag driven byextra.google_site_verificationinmkdocs.yml.alternateName,installUrl,sameAsandisAccessibleForFree, and broadens the keywords.descriptionfront-matter field to the API reference pages,contributing.mdanduser-guide/discrete.md.site_descriptionnow mentions both continuous and discrete optimization, and the homepage title is now "Local Optima Networks in Python".README
Parallel sampling docs (
user-guide/sampling.md)n_jobsvalueBasinHoppingSamplerandILSSamplerorder results and time progress callbacksn_jobsexplanations in the Basin-Hopping, ILS anddiscrete.mdconfig tables now link to that section.Docstrings
preserve_runs_without_transitionsdocstrings in both samplers, because that earlier behaviour was never defined.