A real symmetric matrix is positive definite when its quadratic form is positive on every nonzero vector. By the spectral theorem, this is equivalent to all its eigenvalues being positive. It has a unique symmetric positive-definite square root.
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- Center of an ellipse
- Cholesky factor
- Condition-number bound for preconditioned steepest descent
- Conjugate direction
- Conjugate gradient convergence bound
- Controllability Gramian
- Diagonal scaling for a two-dimensional linear system
- Dirichlet Poisson solution by tridiagonal elimination
- Energy-norm proof of convergence of SOR
- Fill-in
- Finite termination of conjugate directions
- Gauss-Seidel convergence from diagonal dominance and positive definiteness
- Gauss-Seidel energy decrease identity
- Gauss-Seidel iteration for a two-variable positive-definite system
- Gauss-Seidel method
- Jacobi preconditioner
- Krylov subspace method
- Lanczos algorithm
- Left preconditioning
- Matrix norm induced by an energy norm
- Matrix preconditioner
- Matrix preconditioning
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- Preconditioned steepest descent
- Principal axes of an ellipse
- Quadratic-form ellipse area
- Quadratic minimization along a line
- Safe pivots for tridiagonal elimination
- Sparse elimination of a four-leaf star
- Sparse Gaussian elimination
- Spectral weighting of conjugate gradient error
- Steepest-descent step size
- Symmetric positive-definite preconditioning
- Symmetric preconditioning as energy geometry
- Weighted Euclidean inner product
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