markbaker/matrix
PHPMatrix is a PHP matrix algebra library supporting addition, subtraction, multiplication, division, determinants, inverses, transpose, trace, solve (A·X=B), and LU/QR decompositions. Install via Composer as markbaker/matrix and use Matrix objects from arrays.
Architecture fit: Pure PHP library with Composer integration fits Laravel's service-oriented architecture. Ideal for niche use cases like scientific computing, recommendation systems, or image processing within Laravel applications, but not a standard fit for typical CRUD web apps.
Integration feasibility: Straightforward via Composer; requires encapsulation in Laravel service classes. Immutable objects align with Laravel's stateless HTTP model but need careful handling in long-running processes.
Technical risk: Low activity (last release 2021), zero dependents, and unaddressed TO DO items (e.g., Cholesky decomposition) pose stability risks. PHP 8+ compatibility is unverified for newer Laravel versions.
Key questions: What is the expected matrix size/scale? Are there performance benchmarks for production workloads? Are there alternatives (e.g., native extensions, Python microservices) for heavy linear algebra?
Stack fit: Seamless with Laravel's PHP stack; no external dependencies or server configuration needed. Best suited for dedicated data processing jobs or internal tools rather than high-traffic web endpoints.
Migration path: Install via Composer; use Matrix\Builder for common patterns (identity/filled matrices). Chain immutable operations (e.g., $matrix->transpose()->inverse()) or use static methods. Ensure BC compliance by migrating procedural calls to markbaker/matrix-functions if needed.
Compatibility: PHP 8+ compatibility unconfirmed; verify with Laravel version. Check dimension constraints for operations (e.g., matrix multiplication requires matching inner dimensions). No breaking changes in v3.0 for object-oriented usage.
Sequencing: Start with basic operations (addition, multiplication) in isolated tests. Introduce decompositions (QR/LU) for numerical stability in specific workflows. Avoid high-risk features (e.g., solve() on ill-conditioned matrices) until validation.
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