| { |
| "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" |
| } |
