Update to README documentation for better understanding and Use

The only change in this release was an update to the README.md and other documentation.

Full Changelog: v2.1.0...v2.1.1

Geometric Clustering Minimization with Recursive QM Optimization

This release introduces geometric clustering algorithms that enhance hierarchical Boolean minimization by exploiting spatial relationships within K-map representations. We partition the minimization problem into geometrically coherent subspaces, dramatically reducing the search space for subsequent Quine-McCluskey (QM) optimization.

Core Methodology

The geometric preprocessing stage identifies and merges spatially adjacent minterms through dimensional projection, producing an optimized set of prime implicants for the recursive QM engine. This hybrid approach combines the visual structure of K-maps with the algorithmic rigor of tabular methods, achieving substantial computational advantages over purely algebraic techniques.

Theoretical Findings

Extensive empirical analysis reveals distinct decay characteristics and fundamental complexity limits in geometric clustering:

• 3D Functions: Decay occurs at n > 8 variables
• 4D Functions: Decay occurs at n > 10 variables

Our forthcoming paper demonstrates that geometric approaches exhibit structural collapse beyond 10 variables, establishing theoretical bounds on the applicability of spatial clustering methods for high-dimensional Boolean minimization.

Performance Benchmarks

Comparative analysis against PyEDA's espresso_exprs() reveals exceptional efficiency:

• 3D Functions: 4% deviation at maximum variable count (n = 8)
• 4D Functions: 6% deviation at maximum variable count (n = 10)

These metrics demonstrate near-optimal minimization quality while maintaining computational tractability for practical circuit design.

Broader Implications

This release bridges geometric intuition with algebraic optimization, achieving state-of-the-art performance in logic synthesis. It provides foundations for understanding information loss in Boolean autoencoders and the topological implications of information structures.

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Stanislus Emeka-Onwuneme
CTO, Stan's Technologies

StanLogic v2.0.0 (Beta)

This latest version introduces the concept of multidimensionality, allowing the use of 3D Karnaugh Maps for 5-16 variable functions and 4D Karnaugh Maps for funtions greater than 16 variables.

It introduces a parallel processing approach to handle such large computations and ensure program efficiency.

The documentation is still in the works, and I'm doing more tests to ensure the programs are as robust before releasing the official version.

Regards,
Stanislus.

StanLogic v1.1.0

This new release improved on one of the available packages: StanLogic, in particular the module - kmapsolver

Key Additions:

1. Optimized performance by substituting frozen set operations with complexity O(n) with bitmask operations O(1)

2. Included extensive test files with SymPy as a benchmark

stangorithms/0.1.2

Added kmaps_algorithms which provides tools for automating the simplification of Boolean functions using Karnaugh maps (K-maps). It supports generating K-maps for variables up to 4, identifying prime implicants, and deriving minimal Sum-of-Products expressions, making the process fast and error-free.