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Benchmark the operations that dominate robotics and computer vision inner loops: fixed-size matrix multiply, matrix-vector, inverse, determinant, LLT, LDLT, PartialPivLU, ColPivHouseholderQR, JacobiSVD, and SelfAdjointEigenSolver for sizes 2x2 through 8x9. Key findings from the baseline measurements: - MatMul/MatVec: excellent (<1ns for 3x3 float) - Inverse 3x3: excellent (3.4ns) - LLT 3x3→4x4: 8x jump (3.9→31.7ns float) due to inlining threshold - ColPivQR 3x3: 166ns — expensive for such a small matrix - JacobiSVD 3x3: 498ns double — the main CV bottleneck - SelfAdjointEig: computeDirect() is 3.2x faster than iterative for 3x3 (71ns vs 230ns) — many users may not know this API exists Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
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