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Lev Yohananov

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#software testing Open access Sep 2026

Computational Supplement to Exact Values and Saturation of Graph Learning Complexity

Documentation-only version 1.0.1 of the reproducibility software and aggregate verification records supporting Proposition 9.2 (finite verification record) of Exact Values and Saturation of Graph Learning Complexity. The manuscript was previously titled Saturation of Learning Complexity and Complete Multipartite Graphs. This version updates the software title, version-specific identifier and current-manuscript concordance while preserving the original 19-page release snapshot. All scientific code, tests, validator and aggregate outputs are byte-identical to version 1.0.0. No manuscript PDF or LaTeX source is included. The package contains an exact quotient dynamic program and a labeled evaluator for complete bipartite graphs, rational-weight and integer-parameter regression suites, complete multipartite isoperimetric checks, and an evaluator importing no quotient-solver code for all connected labeled simple graphs on two through five vertices. It includes input domains, expected outputs, a claim-to-artifact map, checksums and smoke/full reproduction instructions. The bipartite evaluators share a module and cut helper; no full implementation independence is claimed. Recorded checks cover 600 complete bipartite graphs and 113,250 quotient states, 3,456 rational-weight instances and 560 integer-parameter instances, and 3,855 weighted instances over 771 connected labeled graphs. Computations use exact integer or rational arithmetic, Python 3.10 or newer and only the standard library. The records are aggregate deterministic finite-suite summaries, not per-instance certificates or proofs of unbounded theorems. Later star, two-vertex-shore, cactus and general-subset results have deductive proofs; their later private diagnostic code and a new general-subset implementation are not included or promised.

Lev Yohananov · 0 citations
#software testing Open access Sep 2026

Exact verification for consecutive optimal components in 2-club cluster edge deletion on proper interval graphs

This software deposit contains a deterministic standard-library Python verifier and reproduction instructions for consecutive optimal components in unweighted 2-Club Cluster Edge Deletion on proper interval graphs. It includes no manuscript, precomputed result records, external datasets, or certificate archive. The software generates the finite graph inputs and can generate result records and regression certificates when executed. The full computation enumerates 23,712 fixed-order umbrella representations through ten vertices, compares distinct optimization paths through eight vertices, and tests the endpoint-reach criterion. These finite checks are diagnostic evidence and do not replace the mathematical proof. The optimization paths share graph generation, low-level helpers, and final validators. Python 3.12 or later is recommended. The package documents a quick smoke test and the full exact run, with no third-party dependencies or external input downloads.

Avraham Kreindel, Lev Yohananov · 0 citations
#software testing Open access Sep 2026

Exact verification for consecutive optimal components in 2-club cluster edge deletion on proper interval graphs

This software deposit contains a deterministic standard-library Python verifier and reproduction instructions for consecutive optimal components in unweighted 2-Club Cluster Edge Deletion on proper interval graphs. It includes no manuscript, precomputed result records, external datasets, or certificate archive. The software generates the finite graph inputs and can generate result records and regression certificates when executed. The full computation enumerates 23,712 fixed-order umbrella representations through ten vertices, compares distinct optimization paths through eight vertices, and tests the endpoint-reach criterion. These finite checks are diagnostic evidence and do not replace the mathematical proof. The optimization paths share graph generation, low-level helpers, and final validators. Python 3.12 or later is recommended. The package documents a quick smoke test and the full exact run, with no third-party dependencies or external input downloads.

Avraham Kreindel, Lev Yohananov · 0 citations

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