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39 changes: 39 additions & 0 deletions assets/site.css
Original file line number Diff line number Diff line change
Expand Up @@ -429,6 +429,45 @@ h4 { font-size: .8125rem; }
.copy-btn svg { width: 14px; height: 14px; }
.copy-btn[data-copied] { color: var(--accent-2); border-color: var(--accent); }

/* A measured table inside a project block. The caption carries the conditions,
so the figures above it can be read without the repository open. */
.measure {
margin: 0 0 24px;
overflow-x: auto;
}
.measure table {
width: 100%;
border-collapse: collapse;
font-variant-numeric: tabular-nums;
}
.measure caption {
caption-side: bottom;
padding-top: 12px;
text-align: left;
font-size: .8125rem;
line-height: 1.5;
color: var(--fg-3);
}
.measure th {
text-align: left;
font-size: .6875rem;
font-weight: 600;
letter-spacing: .12em;
text-transform: uppercase;
color: var(--fg-3);
padding: 0 20px 10px 0;
border-bottom: 1px solid var(--line-2);
}
.measure td {
padding: 11px 20px 11px 0;
border-bottom: 1px solid var(--line);
font-size: .9375rem;
}
.measure td:first-child { color: var(--fg-2); }
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.measure code { color: var(--fg-2); }

/* --- Course --------------------------------------------------------------- */

.course {
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23 changes: 23 additions & 0 deletions index.html
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Expand Up @@ -298,6 +298,29 @@ <h3><a href="https://github.com/DrobyshevDev/stadion" rel="noopener">stadion</a>
with a confidence interval and placed on that scale, so «better» means a distance from
the best achievable result rather than a position on a leaderboard.
</p>

<div class="measure">
<table>
<caption>Headroom between the tuned classical method and the computed optimum, with a 95% bootstrap interval on the paired difference.</caption>
<thead>
<tr><th>Task</th><th>Headroom</th><th>95% interval</th></tr>
</thead>
<tbody>
<tr><td><code>inventory</code></td><td>+0.4%</td><td>[+0.561, +1.031]</td></tr>
<tr><td><code>joint-pricing</code></td><td>+4.3%</td><td>[+3.794, +5.656]</td></tr>
<tr><td><code>pricing</code></td><td>+6.2%</td><td>[+1.265, +1.761]</td></tr>
<tr><td><code>queueing</code></td><td>+16.9%</td><td>[+3.418, +3.988]</td></tr>
<tr><td><code>supply-chain</code></td><td>+17.3%</td><td>[+5.505, +7.493]</td></tr>
<tr><td><code>energy</code></td><td>+26.6%</td><td>[+4.221, +4.690]</td></tr>
</tbody>
</table>
</div>
<p>
The spread is the point. Where the textbook answer is already near-optimal there
is almost nothing to win; where it cannot plan ahead, a quarter of the value is
still on the table. A benchmark whose tasks all leave generous headroom has
selected for problems the standard method happens to be bad at.
</p>
<div class="project-links">
<a href="https://github.com/DrobyshevDev/stadion" rel="noopener">Repository</a>
<a href="https://pypi.org/project/stadion-rl/" rel="noopener">PyPI</a>
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23 changes: 23 additions & 0 deletions ru/index.html
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Expand Up @@ -300,6 +300,29 @@ <h3><a href="https://github.com/DrobyshevDev/stadion" rel="noopener">stadion</a>
выдаётся с доверительным интервалом и ставится на эту шкалу, поэтому «лучше» означает
расстояние до достижимого предела, а не позицию в таблице лидеров.
</p>

<div class="measure">
<table>
<caption>Запас между настроенным классическим методом и посчитанным оптимумом, с 95% бутстрап-интервалом на парной разности.</caption>
<thead>
<tr><th>Задача</th><th>Запас</th><th>95% интервал</th></tr>
</thead>
<tbody>
<tr><td><code>inventory</code></td><td>+0.4%</td><td>[+0.561, +1.031]</td></tr>
<tr><td><code>joint-pricing</code></td><td>+4.3%</td><td>[+3.794, +5.656]</td></tr>
<tr><td><code>pricing</code></td><td>+6.2%</td><td>[+1.265, +1.761]</td></tr>
<tr><td><code>queueing</code></td><td>+16.9%</td><td>[+3.418, +3.988]</td></tr>
<tr><td><code>supply-chain</code></td><td>+17.3%</td><td>[+5.505, +7.493]</td></tr>
<tr><td><code>energy</code></td><td>+26.6%</td><td>[+4.221, +4.690]</td></tr>
</tbody>
</table>
</div>
<p>
Разброс здесь и есть содержание. Там, где учебный ответ уже почти оптимален,
выигрывать нечего; там, где он не умеет планировать вперёд, на столе остаётся
четверть ценности. Бенчмарк, у которого все задачи оставляют щедрый запас,
отобран под проблемы, на которых стандартный метод плох.
</p>
<div class="project-links">
<a href="https://github.com/DrobyshevDev/stadion" rel="noopener">Репозиторий</a>
<a href="https://pypi.org/project/stadion-rl/" rel="noopener">PyPI</a>
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