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| <title>Large-Scale Optimization Decomposition Lab | Aria AI</title>
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| :root {
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| <body>
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| <section class="hero">
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| <div class="badge">ARIA AI · OPERATIONS RESEARCH · DECOMPOSITION</div>
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| <h1><span>Large-Scale Optimization Decomposition Lab</span></h1>
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| <p class="subtitle">Compare Benders, Dantzig-Wolfe, Column Generation, Lagrangian Relaxation, Progressive Hedging, and ADMM on industrial-scale problem families — with pre-computed benchmarks and a method selector.</p>
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| <div class="links">
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| <a class="cta" href="https://huggingface.co/datasets/alirezaaminzadeh/decompbench-benchmark-data">Dataset</a>
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| <a class="cta-out" href="https://huggingface.co/alirezaaminzadeh/decompbench-method-selector">Method Selector</a>
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| <a class="cta-out" href="gradio/">Gradio Console</a>
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| <a class="cta-out" href="https://aria-ai.ir">Aria AI</a>
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| </div>
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| </section>
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| <div class="container">
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| <div class="kpi-grid">
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| <div class="kpi"><div class="val" id="k-problems">7</div><div class="lbl">Problem Families</div></div>
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| <div class="kpi"><div class="val" id="k-methods">7</div><div class="lbl">Methods</div></div>
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| <div class="kpi"><div class="val" id="k-metrics">9</div><div class="lbl">Metrics</div></div>
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| <div class="kpi"><div class="val" id="k-runs">—</div><div class="lbl">Benchmark Rows</div></div>
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| <div class="kpi"><div class="val" id="k-version">—</div><div class="lbl">Engine</div></div>
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| </div>
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| <h2>Decomposition Methods</h2>
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| <div class="method-grid" id="methods-grid"></div>
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| <h2>Benchmark Results</h2>
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| <p class="note">Filter by problem family. Winners highlighted in green.</p>
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| <label>Problem: <select id="problem-filter"></select></label>
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| <div style="overflow-x:auto">
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| <table>
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| <thead>
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| <tr>
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| <th>Problem</th><th>Size</th><th>Method</th><th>Gap %</th>
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| <th>First Sol (s)</th><th>Target Gap (s)</th><th>Iterations</th><th>Cols/Cuts</th><th>LB Quality</th>
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| </tr>
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| </thead>
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| <tbody id="bench-body"></tbody>
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| </table>
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| </div>
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| </div>
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| <footer class="footer">© Aria AI Operations Research Team · MIT License</footer>
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| <script>
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| const METHODS = {
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| benders: { label: "Benders Decomposition", desc: "Strategic master + operational subproblems via cuts." },
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| dantzig_wolfe: { label: "Dantzig-Wolfe", desc: "Block-angular master with pricing subproblems." },
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| column_generation: { label: "Column Generation", desc: "Generate useful columns on demand." },
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| lagrangian_relaxation: { label: "Lagrangian Relaxation", desc: "Dualize hard coupling constraints." },
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| progressive_hedging: { label: "Progressive Hedging", desc: "Scenario consensus for stochastic programs." },
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| admm: { label: "ADMM", desc: "Distributed consensus across independent units." },
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| direct: { label: "Monolithic MIP", desc: "Full model baseline without decomposition." }
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| };
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| async function load() {
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| const summary = await fetch("assets/demo/summary.json").then(r => r.json());
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| document.getElementById("k-runs").textContent = summary.benchmark_rows;
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| document.getElementById("k-version").textContent = "v" + summary.engine_version;
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| const grid = document.getElementById("methods-grid");
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| Object.entries(METHODS).forEach(([id, m]) => {
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| if (id === "direct") return;
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| const d = document.createElement("div");
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| d.className = "method-card";
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| d.innerHTML = `<h3>${m.label}</h3><p>${m.desc}</p>`;
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| grid.appendChild(d);
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| });
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| const data = await fetch("assets/demo/benchmarks.json").then(r => r.json());
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| const rows = data.rows || [];
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| const problems = [...new Set(rows.map(r => r.problem_type))];
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| const sel = document.getElementById("problem-filter");
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| problems.forEach(p => {
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| const o = document.createElement("option");
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| o.value = p; o.textContent = rows.find(r => r.problem_type === p)?.problem_label || p;
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| sel.appendChild(o);
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| });
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| function render() {
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| const body = document.getElementById("bench-body");
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| body.innerHTML = "";
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| rows.filter(r => r.problem_type === sel.value && r.size === "medium" && r.seed === 42)
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| .sort((a,b) => a.optimality_gap - b.optimality_gap)
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| .forEach(r => {
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| const tr = document.createElement("tr");
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| tr.innerHTML = `
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| <td>${r.problem_label}</td><td>${r.size}</td>
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| <td class="${r.winner ? 'winner' : ''}">${r.method_label}</td>
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| <td>${r.optimality_gap.toFixed(2)}</td>
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| <td>${r.time_to_first_solution.toFixed(3)}</td>
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| <td>${r.time_to_target_gap.toFixed(3)}</td>
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| <td>${r.iterations}</td>
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| <td>${r.columns_or_cuts}</td>
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| <td>${r.lower_bound_quality.toFixed(2)}</td>`;
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| body.appendChild(tr);
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| });
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| }
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| sel.addEventListener("change", render);
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| render();
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| }
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| load().catch(e => console.error(e));
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| </script>
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| </body>
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| </html>
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