{
  "schema_version": 1,
  "title": "Reproduction: Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes",
  "emoji": "🎯",
  "space_id": "ProCreations/repro-randomized-feasibility-methods-for-constrained-optimization-with-adaptive-step-sizes",
  "paper": {
    "arxiv_id": "2601.20076"
  },
  "tags": [
    "icml2026-repro",
    "paper-1BchRVONfp"
  ],
  "updated_at": "2026-07-19T19:59:22+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "executive-summary",
        "title": "Executive summary",
        "file": "pages/executive-summary/page.md",
        "children": []
      },
      {
        "slug": "claim-1-algorithm-2-reaches-prescribed-tolerance-with-linear-convergence-in-expectation-on-strongly-convex-smooth-objectives-theorem-4-4",
        "title": "Claim 1: Algorithm 2 reaches prescribed tolerance with linear convergence in expectation on strongly convex smooth objectives (Theorem 4.4)",
        "file": "pages/claim-1-algorithm-2-reaches-prescribed-tolerance-with-linear-convergence-in-expectation-on-strongly-convex-smooth-objectives-theorem-4-4/page.md",
        "children": []
      },
      {
        "slug": "claim-2-algorithm-3-dows-with-randomized-feasibility-exhibits-the-o-1-sqrt-t-worst-case-horizon-scaling-for-convex-nonsmooth-objectives-theorem-5-3",
        "title": "Claim 2: Algorithm 3 DoWS with randomized feasibility exhibits the O(1/sqrt(T)) worst-case horizon scaling for convex nonsmooth objectives (Theorem 5.3)",
        "file": "pages/claim-2-algorithm-3-dows-with-randomized-feasibility-exhibits-the-o-1-sqrt-t-worst-case-horizon-scaling-for-convex-nonsmooth-objectives-theorem-5-3/page.md",
        "children": []
      },
      {
        "slug": "claim-3-algorithm-4-t-dows-retains-the-adaptive-rate-without-requiring-bounded-y-theorem-5-5",
        "title": "Claim 3: Algorithm 4 T-DoWS retains the adaptive rate without requiring bounded Y (Theorem 5.5)",
        "file": "pages/claim-3-algorithm-4-t-dows-retains-the-adaptive-rate-without-requiring-bounded-y-theorem-5-5/page.md",
        "children": []
      },
      {
        "slug": "claim-4-randomized-feasibility-produces-geometric-infeasibility-reduction-with-the-number-of-updates-lemma-3-1",
        "title": "Claim 4: Randomized feasibility produces geometric infeasibility reduction with the number of updates (Lemma 3.1)",
        "file": "pages/claim-4-randomized-feasibility-produces-geometric-infeasibility-reduction-with-the-number-of-updates-lemma-3-1/page.md",
        "children": []
      },
      {
        "slug": "claim-5-paper-scale-qcqp-simulations-reproduce-predicted-function-value-and-infeasibility-decay",
        "title": "Claim 5: Paper-scale QCQP simulations reproduce predicted function-value and infeasibility decay",
        "file": "pages/claim-5-paper-scale-qcqp-simulations-reproduce-predicted-function-value-and-infeasibility-decay/page.md",
        "children": []
      },
      {
        "slug": "claim-6-algorithms-3-and-4-provide-parameter-free-constrained-optimization-without-problem-specific-step-size-tuning",
        "title": "Claim 6: Algorithms 3 and 4 provide parameter-free constrained optimization without problem-specific step-size tuning",
        "file": "pages/claim-6-algorithms-3-and-4-provide-parameter-free-constrained-optimization-without-problem-specific-step-size-tuning/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 1940,
  "revision": "1784491162351296000"
}