Matrix Laravel Package

Getting Started

Start by installing the package via Composer: composer require markbaker/matrix:^3.0. Create a Matrix instance from a 2D PHP array—e.g., $matrix = new Matrix\Matrix([[1,2],[3,4]]);. The core day-to-day use case is performing linear algebra operations common in scientific or financial computation: addition, multiplication, inversion, and solving systems of equations (e.g., $A->solve($B) for AX = B). Use Matrix\Builder for convenience: Builder::createIdentityMatrix(4) or Builder::createFilledMatrix(0, 5, 3) for fast initialization. First stop: read the README’s “Usage” section—it’s concise, includes working examples, and clarifies the immutability model.

Implementation Patterns

• Fluent, chainable operations: Leverage immutability for readable pipelines:
  $result = $A->transpose()->multiply($B->inverse())->add($C);
• Hybrid method styles: Use instance methods for clarity ($A->determinant()) and static helpers (Operations::multiply($A, $B)) when passing arrays or chaining in mixed-type contexts.
• Solving systems robustly: Prefer decomposition-based solving for numerical stability:
  $qr = new Matrix\Decomposition\QR($designMatrix);
$coefficients = $qr->solve($responseVector);
  Especially useful in regression or interpolation logic in data-heavy Laravel jobs.
• Decomposition reuse: Factor expensive matrices once and solve multiple RHS vectors:
  $lu = new Matrix\Decomposition\LU($A);
$x1 = $lu->solve($b1); $x2 = $lu->solve($b2);
• Integration with Laravel: Wrap matrix logic in dedicated services (e.g., LinearRegressionService), convert via ->toArray(), and hydrate DTOs or collections (collect($matrix->toArray())->flatten()).

Gotchas and Tips

• Immutability enforced: No side effects—methods return new instances. Remember to reassign: $A = $A->multiply($B);. Forgetting this causes subtle bugs.
• Dimension validation is your responsibility: Check getRows()/getColumns() before calling multiply() or add(). Wrap operations in try/catch (Matrix\Exceptions\MatrixException) since dimension mismatches throw immediately.
• v3.0 BC break: If you’re using legacy procedural calls like matrix_multiply($A, $B), migrate to markbaker/matrix-functions. This package does not provide them.
• Floating-point tolerance: Use assertEquals($expected, $actual->toArray(), '', 1e-9) in tests to ignore precision noise—default tolerances are not set for you.
• Null/sparse edge cases: Functions like trace() or determinant() assume a non-empty square matrix. Use isEmpty() or isValid() guards first.
• Performance caution: Matrix size matters. For grids > 1000×1000 or real-time APIs, profile—this is pure PHP and will bottleneck compared to native extensions. Consider offloading heavy work to queued jobs or a Python microservice.