I’m releasing malloc (MultiplicativeConstraint), a production-grade, open-source library for solving large, constraint-rich partitioning problems—think resource allocation, scheduling, clustering, supply chain, EDA, and HPC.

Why is this interesting?

Physics-inspired math: Uses spectral theory and heat-kernel action minimization—correlation with target objectives is ρ ≈ 0.996.

Enterprise scalability: Sparse matrix core solves 100,000-node problems in under 90 seconds with only 23MB memory.

Adaptable constraints: Neural module adapts to real-world, contradictory constraints with up to 83% satisfaction.

Key applications: Cloud resource allocation, HPC load balancing, clustering, logic optimization, graph partitioning, bin packing, and more.

Limitations: Not a general P=NP solution; works best when structural regularity exists.

Replicable results: All benchmarks, proofs (MATH.md), and performance demos are open in the repo.

Try it out: Repo + docs: https://codeberg.org/aninokuma/malloc

README covers the API, benchmarks, and supported problems.