| { | |
| "schema_version": 1, | |
| "title": "Reproduction: A Fully First-Order Layer for Differentiable Optimization", | |
| "emoji": "📊", | |
| "space_id": "snaykey/repro-first-order-diffopt", | |
| "paper": { | |
| "openreview_id": "jJur8Fq7IK" | |
| }, | |
| "tags": [ | |
| "icml2026-repro", | |
| "paper-jJur8Fq7IK" | |
| ], | |
| "updated_at": "2026-08-01T00:00:00Z", | |
| "root": { | |
| "slug": "index", | |
| "title": "Reproduction: A Fully First-Order Layer for Differentiable Optimization", | |
| "file": "pages/index.md", | |
| "children": [ | |
| { | |
| "slug": "executive-summary", | |
| "title": "Executive summary", | |
| "file": "pages/executive-summary/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-1-sec42-alg1-firstorder-oracle", | |
| "title": "FFOLayer computes an ε-approximate hypergradient using an active-set Lagrangian oracle that requires no Hessian evaluations, achieving Õ(1) first-order oracle calls per hypergradient estimate (Section 4.2, Algorithm 1).", | |
| "file": "pages/claim-1-sec42-alg1-firstorder-oracle/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-2-thm41-ghost-reformulation-preserves-hypergradient", | |
| "title": "Theorem 4.1 proves that the 'ghost bilevel optimization' reformulation, which treats active constraints as equalities, preserves the accuracy of the hypergradient computed at the original constrained-optimization solution (Section 4.1, Theorem 4.1).", | |
| "file": "pages/claim-2-thm41-ghost-reformulation-preserves-hypergradient/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-3-sec4-oracle-complexity", | |
| "title": "For constrained bilevel optimization, the method achieves an oracle complexity of Õ(δ⁻¹ε⁻³), matching best-known rates for non-smooth non-convex optimization, while extending prior guarantees from linear to general convex constraints (Section 4, complexity analysis).", | |
| "file": "pages/claim-3-sec4-oracle-complexity/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-4-dfl-qp-sudoku-convergence-faster-backward", | |
| "title": "On synthetic decision-focused-learning QP tasks and 9×9 Sudoku constraint-learning tasks formulated as linear programs, FFOLayer matches the convergence of exact differentiable-optimization solvers CvxpyLayer and qpth while using a substantially faster backward pass (Experiments section, synthetic QP and Sudoku benchmarks).", | |
| "file": "pages/claim-4-dfl-qp-sudoku-convergence-faster-backward/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-5-objective-agnostic-detach-coefficient", | |
| "title": "FFOLayer's PyTorch implementation is objective-agnostic, exposing task-loss influence via a single detached gradient coefficient c := detach(dF/dy*), allowing users to substitute it for CvxpyLayer with minimal code changes (Section on practical implementation).", | |
| "file": "pages/claim-5-objective-agnostic-detach-coefficient/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-6-outperforms-lpgd-no-hessian-inversion", | |
| "title": "FFOLayer outperforms the gradient-unrolling baseline LPGD in the reported experiments while eliminating the cubic-complexity Hessian inversion required by standard implicit differentiation (Experiments section, comparison with LPGD).", | |
| "file": "pages/claim-6-outperforms-lpgd-no-hessian-inversion/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "conclusion", | |
| "title": "Conclusion", | |
| "file": "pages/conclusion/page.md", | |
| "children": [] | |
| } | |
| ] | |
| } | |
| } |