Linear Systems

This page documents verified linear system solvers.

Overview

Verified linear system solvers provide rigorous enclosures for solutions of $Ax = b$ with guaranteed error bounds. Multiple methods are available depending on the matrix properties and desired tightness of bounds.

Krawczyk Method

The Krawczyk method uses interval Newton iteration to verify and refine solutions. See src/linear_system/Krawczyk.jl for the basic implementation.

HBR Method

The Hansen-Bliek-Rohn enclosure method provides componentwise bounds.

Gaussian Elimination

Verified Gaussian elimination with interval pivoting.

Iterative Methods

Interval iterative methods for large sparse systems.

Preconditioning

Preconditioning strategies for improving convergence.

Preconditioner types (see PreconditionerType):

  • MidpointInverse - Inverse of midpoint matrix
  • LUFactorization - LU-based preconditioner
  • LDLTFactorization - LDLT-based preconditioner (symmetric)
  • IdentityPreconditioner - No preconditioning

Overdetermined Systems

Least squares solutions for overdetermined systems.

Shaving

Interval shaving for tightening bounds.

H-Matrix Systems

Verified solvers for H-matrices (hierarchical matrices).

Matrix Regularity

Functions to verify matrix regularity (invertibility).

Determinant Bounds

Rigorous bounds on matrix determinants.