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MatrixTransformer – A Unified Symbolic Framework for Structural Matrix Transformations (GitHub + Research Paper)

by fikayoAy
13th Jul 2025
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Hi everyone,

Over the past few months, I’ve been working on a new library and research paper that unify structure-preserving matrix transformations within a high-dimensional framework (hypersphere and hypercubes).

Today I’m excited to share: MatrixTransformer a Python library and paper built around a 16-dimensional decision hypercube that enables smooth, interpretable transitions between matrix types like

  • Symmetric
  • Hermitian
  • Toeplitz
  • Positive Definite
  • Diagonal
  • Sparse
  • ...and many more

It is a lightweight, structure-preserving transformer designed to operate directly in 2D and nD matrix space, focusing on:

  • Symbolic & geometric planning
  • Matrix-space transitions (like high-dimensional grid reasoning)
  • Reversible transformation logic
  • Compatible with standard Python + NumPy

It simulates transformations without traditional training—more akin to procedural cognition than deep nets.

What’s Inside:

  • A unified interface for transforming matrices while preserving structure
  • Interpolation paths between matrix classes (balancing energy & structure)
  • Benchmark scripts from the paper
  • Extensible design—add your own matrix rules/types
  • Use cases in ML regularization and quantum-inspired computation

Links:

Paper: https://zenodo.org/records/15867279
Code: https://github.com/fikayoAy/MatrixTransformer
Related: [quantum_accel]—a quantum-inspired framework evolved with the MatrixTransformer framework link: fikayoAy/quantum_accel

If you’re working in machine learning, numerical methods, symbolic AI, or quantum simulation, I’d love your feedback.
Feel free to open issues, contribute, or share ideas.

Thanks for reading!