LU Factorization Calculator


Matrices

📥 Export options

Exporting...

⭐ Rate this tool:

★ ★ ★ ★ ★

⭐ 5.0/5 (5 votes)



Master matrix decomposition techniques for efficient solving of linear systems and advanced matrix operations

Introduction to LU Factorization

Definition

LU factorization (also called LU decomposition) is a matrix decomposition technique that factors a square matrix A into the product of a lower triangular matrix L and an upper triangular matrix U. Mathematically: A = LU, where L has ones on the diagonal and zeros above, while U has zeros below the diagonal.

This fundamental technique in numerical linear algebra transforms the problem of solving linear systems into a sequence of simpler triangular system solves. The method is particularly powerful because it separates the expensive elimination process from the relatively cheap substitution steps, enabling efficient solution of multiple systems with the same coefficient matrix.

A = LU = [lower triangular] × [upper triangular]

System Solving

Solve Ax = b efficiently by solving Ly = b, then Ux = y

Matrix Inversion

Compute A⁻¹ by solving multiple triangular systems

Determinant Calculation

det(A) = det(L) × det(U) = product of U's diagonal elements

Multiple Right-Hand Sides

Reuse factorization for different b vectors without refactoring

Mathematical Foundation

Basic LU Decomposition

For a square matrix A, the LU factorization expresses A as:

A = LU

where L = [l₁₁ 0 0 ...]
[l₂₁ l₂₂ 0 ...]
[l₃₁ l₃₂ l₃₃ ...]
[... ... ... ...]

and U = [u₁₁ u₁₂ u₁₃ ...]
[ 0 u₂₂ u₂₃ ...]
[ 0 0 u₃₃ ...]
[... ... ... ...]

Existence and Uniqueness Theorem

A square matrix A has an LU factorization if and only if all its leading principal minors are nonzero. When the factorization exists, it is unique if we require L to have ones on the diagonal (Doolittle factorization) or U to have ones on the diagonal (Crout factorization).

Variants of LU Factorization

Doolittle Method

L has unit diagonal (lᵢᵢ = 1), U has arbitrary diagonal elements

Crout Method

U has unit diagonal (uᵢᵢ = 1), L has arbitrary diagonal elements

LUP Factorization

Includes permutation matrix P: PA = LU for numerical stability

Block LU

Decomposition of block matrices for large-scale computations

Detailed Example

Let's decompose the matrix:

A = [4 3 2]
[3 4 1]
[2 1 3]

Step 1: Compute First Row of U

U₁ = [4   3   2]
     [0   0   0]
     [0   0   0]

L₁ = [1   0   0]
     [0   1   0]
     [0   0   1]

Step 2: Compute First Column of L

l₂₁ = a₂₁/u₁₁ = 3/4 = 0.75
l₃₁ = a₃₁/u₁₁ = 2/4 = 0.5

L₂ = [1.00  0    0]
     [0.75  1    0]
     [0.50  0    1]

Step 3: Complete the Factorization

Final Result:

L = [1.000   0      0    ]    U = [4.000  3.000   2.000]
    [0.750   1      0    ]        [0      1.750  -0.500]
    [0.500  -0.286  1    ]        [0      0       1.857]

LU Factorization with Partial Pivoting (LUP)

When the standard LU factorization encounters zero or small pivot elements, partial pivoting is essential for numerical stability:

LUP Factorization

Every nonsingular matrix A can be factored as PA = LU, where P is a permutation matrix, L is unit lower triangular, and U is upper triangular. This factorization always exists and is numerically stable.

Applications and Use Cases

Scientific Computing

Finite Element Analysis: Solving large sparse systems in engineering simulations

Computational Fluid Dynamics: Pressure correction algorithms

Computer Graphics

Animation Systems: Solving constraint systems for character rigging

Mesh Processing: Discrete Laplacian operators

Machine Learning

Linear Regression: Normal equation solutions

Gaussian Processes: Covariance matrix inversions

Financial Modeling

Portfolio Optimization: Quadratic programming formulations

Risk Management: Correlation matrix computations

Signal Processing

Digital Filters: System response calculations

Fourier Analysis: Fast convolution algorithms

Circuit Analysis

SPICE Simulators: Modified nodal analysis

Network Theory: Impedance calculations

Frequently Asked Questions

When does LU factorization fail to exist?
LU factorization fails when a leading principal minor is zero. This happens when the matrix has a zero pivot during elimination without row exchanges. The solution is to use LU with partial pivoting (LUP), which always exists for non-singular matrices.
Why use LU instead of directly solving the system with Gaussian elimination?
LU factorization separates the expensive elimination (O(n³)) from the cheap substitution (O(n²)). For multiple systems with the same coefficient matrix, you factor once and solve many times, achieving significant computational savings.
How do I choose between Doolittle and Crout factorizations?
The choice is mostly conventional. Doolittle (unit lower triangular) is more common in computer science, while Crout (unit upper triangular) is sometimes preferred in engineering. Both require the same computational effort and storage.
Is partial pivoting always sufficient for stability?
Partial pivoting is sufficient for most practical matrices, but pathological cases exist where it fails. Complete pivoting guarantees stability but is too expensive. In practice, partial pivoting with iterative refinement is often used when higher accuracy is needed.
How does LU factorization handle singular matrices?
For singular matrices, LU factorization will encounter a zero pivot at some stage. This indicates the matrix is not invertible. You can still obtain a factorization showing the rank deficiency, but the system may have no solution or infinitely many solutions.

Summary and Key Takeaways

Central Insight: LU factorization transforms the challenge of solving linear systems into the simpler problems of solving triangular systems, enabling efficient reuse and providing deep insights into matrix structure.

Final Theorem: The Importance of LU Factorization

LU factorization stands as one of the most important algorithms in computational mathematics, providing the foundation for efficient linear system solution, matrix analysis, and numerous applications across science and engineering. Its combination of theoretical elegance and practical utility makes it an indispensable tool in the numerical analyst's toolkit.