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+#pragma once
+
+#include "matrix.hpp"
+#include "vector.hpp"
+
+namespace linalg {
+
+// Thin QR factorization result.
+//
+// For an m x n matrix A with m >= n:
+//   Q  is m x n with orthonormal columns  (Q^T Q = I_n)
+//   R  is n x n upper triangular
+//   A  = Q * R
+struct QRResult {
+    Matrix Q;
+    Matrix R;
+};
+
+// Classical Gram-Schmidt.
+// Mathematically natural but numerically fragile: orthogonality of Q
+// degrades rapidly on ill-conditioned inputs.
+// Provided for comparison — prefer modified_gs or householder in practice.
+//
+// Throws DimensionMismatchError if rows < cols.
+// Throws SingularMatrixError   if a column is (nearly) linearly dependent.
+QRResult qr_classical_gs(const Matrix& A, double zero_tolerance = 1e-14);
+
+// Modified Gram-Schmidt.
+// Subtracts each projection immediately on the running vector rather than
+// on the original column.  Algebraically equivalent to classical GS but
+// numerically much better — round-off stays local instead of accumulating.
+//
+// Same exceptions as classical GS.
+QRResult qr_modified_gs(const Matrix& A, double zero_tolerance = 1e-14);
+
+// Householder QR.
+// Applies a sequence of orthogonal reflections to zero out below-diagonal
+// entries column by column.  Backward-stable and the standard choice for
+// dense QR.  Works correctly on rank-deficient matrices (zero pivots
+// produce zero diagonal entries in R without throwing).
+//
+// Throws DimensionMismatchError if rows < cols.
+QRResult qr_householder(const Matrix& A);
+
+}  // namespace linalg