LU Decomposition Steps Calculator

Decompose any square matrix into lower and upper factors with transparent row operations and optional partial pivoting visualize every step verify determinants and solve linear systems explore numerical stability export results share insights and learn practical linear algebra with an intuitive responsive interface crafted for students engineers analysts and researchers for projects and exams

Input
Matrix A (3 × 3)
What Is LU Decomposition?

LU decomposition factors a square matrix A into the product L U, sometimes with a permutation matrix P when pivoting is required. Here L is lower triangular with ones on the diagonal, and U is upper triangular. The factorization mirrors Gaussian elimination by recording the elimination multipliers inside L while the transformed system becomes U. After computing L and U once, you can solve Ax = b by forward substitution Ly = Pb followed by back substitution Ux = y. This makes LU ideal for repeated solves with the same matrix, sensitivity analysis, and computing determinants or inverses. Pivoting is important for numerical stability because it avoids zero or tiny pivots that would magnify rounding errors. In practice, libraries return P, L, and U such that PA = LU. Determinants are easy to obtain as the product of the diagonals of U times the sign of the permutation. The method underpins many applications including engineering simulation, optimization, signal processing, and machine learning, and provides a clear, step by step view of how elimination transforms matrices. It also aids error analysis.

Results

Enter your matrix and press Compute LU to view P, L, U and detailed elimination steps.

FAQs
Any square matrix can be attempted. With partial pivoting most nonsingular matrices admit PA = LU. Truly singular or extremely ill conditioned matrices may fail at a pivot.
Pivoting chooses the largest available pivot in magnitude to reduce round off amplification, improving numerical stability and success on nearly singular systems.
L contains the elimination multipliers below the diagonal with ones on the diagonal. These multipliers reconstruct how rows were combined during elimination.
det(A) equals the product of the diagonal entries of U times +1 or −1 depending on whether the number of row swaps is even or odd.
Yes in principle: solve Ly = Pb by forward substitution and then Ux = y by back substitution. This page focuses on factorization and diagnostics.
It measures how closely the computed factors reproduce the permuted matrix. A small Frobenius norm indicates a sound factorization.
A structural zero or catastrophic cancellation can produce a tiny pivot. Pivoting usually avoids this. If it persists, the matrix is likely singular.
Use more decimals to study stability and rounding effects. For clean presentation use 3–6 decimals. The computation itself uses double precision internally.

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