Linear System Calculator
Solve small linear systems Ax = b, plus det, inverse, rank, LU, Cholesky, QR, SVD, least squares, RREF, condition number, nullspace, column space, rowspace, left-nullspace, and eigenvalues. Same engine as the matrix workspace. Not a CAS. Runs locally.
Enter values to calculate.
Trust summary CVP VERIFIED · CVP protocol 1.0.0-proposed · Core assurance · all published modes
- Input interpretation
- Enter values to calculate.
- Result
- —
- Verified scope
- all published modes
- Assurance
- Core
- Declared partition coverage
- PASS · 19/19 declared partitions (solve, det, inverse, multiply, transpose, rank, eigen, lu, chol, qr, svd, lstsq, rref, condition, nullspace, columnspace, rowspace, leftnullspace, invalid-domain) · Matrix
- Known limitations
- Core CVP does not include live graph, viewport, or pointer interaction.
- Model
- Small Ax=b, det, inverse, rank, LU, Cholesky, QR, SVD, least squares, RREF, condition number, nullspace, column space, rowspace, eigen, multiply, transpose.
- Scope
- Real matrices
- Verification
- Engine tested · Source checked · v1.14.16 · CVP VERIFIED · CVP protocol 1.0.0-proposed · Core assurance · all published modes· View Manifest · CVP overview · Specification
- Versions
- Calculation 1.14.16 · CVP protocol 1.0.0-proposed · Evidence 2026-10-30.cholesky-numeric-contract-v2
- Verification revision
- 2026-10-30.cholesky-numeric-contract-v2 · 10/10 property · digest 2f8af10fde34
- Legacy regression
- 58/58 tests · Production surface contract 6/6
- Trust layers
- Verification VERIFIED · Production CURRENT · Overall CVP state: VERIFIED under CVP 1.0.0-proposed
- Reference
- O1 model · O2 expected_values · O2 expected_values · O2 expected_values · O2 expected_values · O2 expected_values · O2 expected_values · O2 numerical_behavior
- Interfaces
- PASS · UI (SSR) / REST / MCP
- Supplemental domain review
- Not performed
- Named expert review
- Not performed
- CVP suite
- 478/478 golden · 8/8 CVP boundary · 78/78 invalid · 10/10 property · 2/2 metamorphic · 3/3 round-trip · 67/67 cross-interface · 2/2 cross-calculator · 6/6 CVP contract · Manifest
- Sources
- ISO 80000-2:2019 Quantities and units — Mathematics
- CRC / Strang — Ax=b, LU/QR/SVD, four fundamental subspaces
- NIST DLMF §3.2 — Linear Algebra
- MIT OpenCourseWare — The Four Fundamental Subspaces
- MIT OpenCourseWare — Orthogonal Matrices and Gram-Schmidt
- MIT OpenCourseWare — Singular Value Decomposition
- MIT OpenCourseWare — Projection Matrices and Least Squares
- NIST DLMF §1.2 — Elementary Algebra
- NumPy — numpy.linalg.cholesky
- Evidence
- 29 legacy golden · 6 legacy boundary · legacy regression suite · 478/478 oracle-backed golden · 78/78 invalid · Artifact integrity PASS
- Semantic contract
- PASS
Full verification
Formulas
Core equations used by this calculator.
How to use
Choose solve, det, inverse, rank, LU, Cholesky, QR, SVD, least squares, RREF, condition number, nullspace, column space, rowspace, left-nullspace, eigen, multiply, or transpose
Solve and least squares need A and rhs b. Multiply needs square A and B of the same order. Det / inverse / rank / LU / Cholesky / QR / SVD / least squares / RREF / condition number / nullspace / column space / rowspace / left-nullspace / eigen also have discovery URLs.
Read x, y (and z) or the matrix
Open /workspace/matrix to keep a local matrix scenario.
Example calculations
Common configurations with formula and result.
2×2
2x + y = 5, x + y = 3
det [[1,2],[3,4]]
1·4 − 2·3
Linear System calculator specification
Version 1.14.16 · Engine tested
- Engine tested 58/58 tests · Production surface contract 6/6
- Named expert review Not performed
- Calculation version 1.14.16
- Definition
- A linear system Ax = b has a unique solution when det(A) ≠ 0. This page is the 2×2 through 4×4 interface of the linear-algebra engine. Larger composition lives at /workspace/matrix.
- What it calculates
- Small Ax=b, det, inverse, rank, LU, Cholesky, QR, SVD, least squares, RREF, condition number, nullspace, column space, rowspace, eigen, multiply, transpose.
- Inputs
- mode
- order or rows+cols for lstsq
- a11…
- b1…
- Outputs
- x
- y
- z
- det
- rank
- eigenvalues
- singular_values
- residual_2
- backward_error
- L
- Q
- status
- rows
- cols
- Formula
Ax = b- Assumptions
- Real matrices
- Order ≤ 4 square for solve/det/inverse/factorizations
- lstsq also A ∈ R^{m×n} with m,n ∈ [2,4]
- Planar ℝ² figure when 2×2; other shapes omit it
- Not a CAS
- Units
- dimensionless
- Boundary conditions
- singular inverse → SINGULAR_MATRIX
- asymmetric Cholesky → NOT_SYMMETRIC; nonpositive pivot → NOT_POSITIVE_DEFINITE
- unknown mode (including jordan-form) → INVALID_MODE
- Example
- 2x+y=5, x+y=3 → x=2, y=1
- Validation cases
2 published on this page · 58/58 tests · Production surface contract 6/6 · View evidence
- mode=solve order=2 a11=2 a12=1 a21=1 a22=1 b1=5 b2=3 → x=2 y=1
- mode=det a11=1 a12=2 a21=3 a22=4 → det=-2
- Sources
- ISO 80000-2:2019 Quantities and units — MathematicsSupports: Mathematical symbols and notation. This standard is not numerical verification or an endorsement of this implementation.
- CRC / Strang — Ax=b, LU/QR/SVD, four fundamental subspacesSupports: LU + partial pivoting solve; det; Gauss–Jordan inverse; rank-nullity; Householder QR; one-sided Jacobi SVD; RREF; κ₁(A)
- NIST DLMF §3.2 — Linear Algebra — §3.2Supports: Gaussian elimination, LU decomposition, conditioning, eigenvalues, and eigenvectors.
- MIT OpenCourseWare — The Four Fundamental SubspacesSupports: Column space, row space, nullspace, left nullspace, and their dimensions.
- MIT OpenCourseWare — Orthogonal Matrices and Gram-SchmidtSupports: Orthogonal matrices and QR factorization; a mathematical reference, not a claim that this implementation uses Gram-Schmidt.
- MIT OpenCourseWare — Singular Value DecompositionSupports: Singular-value decomposition and its relationship to orthogonal bases and matrix rank.
- MIT OpenCourseWare — Projection Matrices and Least SquaresSupports: Least-squares minimization, projections, and normal-equation relationships.
- NIST DLMF §1.2 — Elementary Algebra — Determinants and matrices; systems of linear equationsSupports: Determinants, inverses, matrix identities, and linear-system relationships.
- NumPy — numpy.linalg.choleskySupports: Cholesky factorization A = LLᴴ (or UᴴU) for Hermitian positive-definite matrices, with the real symmetric case A = LLᵀ. This states the factorization and domain, not CalculatorX implementation details. Mathematical reference only; not validation or endorsement of this implementation.
- ISO 80000-2:2019 Quantities and units — Mathematics
- Calculation version
- 1.14.16
Background
Interpretation and common distinctions.
What does this linear system calculator compute?
A linear system Ax = b has a unique solution when det(A) ≠ 0.
The published formula or method is Ax = b. The following references support the stated mathematics or method:
- NIST DLMF §3.2 — Linear Algebra: Gaussian elimination, LU decomposition, conditioning, eigenvalues, and eigenvectors.
- MIT OpenCourseWare — The Four Fundamental Subspaces: Column space, row space, nullspace, left nullspace, and their dimensions.
- MIT OpenCourseWare — Orthogonal Matrices and Gram-Schmidt: Orthogonal matrices and QR factorization; a mathematical reference, not a claim that this implementation uses Gram-Schmidt.
- MIT OpenCourseWare — Singular Value Decomposition: Singular-value decomposition and its relationship to orthogonal bases and matrix rank.
- MIT OpenCourseWare — Projection Matrices and Least Squares: Least-squares minimization, projections, and normal-equation relationships.
- NumPy — numpy.linalg.cholesky: Cholesky factorization A = LLᴴ (or UᴴU) for Hermitian positive-definite matrices, with the real symmetric case A = LLᵀ. This states the factorization and domain, not CalculatorX implementation details. Mathematical reference only; not validation or endorsement of this implementation.
- NIST DLMF §1.2 — Elementary Algebra: Determinants, inverses, matrix identities, and linear-system relationships.
Which inputs and limits should you check?
Rank, inverse, subspace, and least-squares decisions depend on the stated tolerance and conditioning. Inspect residuals and warnings before interpreting a nearly singular matrix.
The worked example uses 2x + y = 5, x + y = 3 with x=2, y=1. Compare it with the Example calculations and the complete result for the same inputs.
How can you verify and cite this result?
CalculatorX is responsible for this page. The mathematical references above explain the model; implementation checks and their scope are documented separately in the math.linear_algebra v1.14.16 Evidence and capability contract. Read the displayed verification and production state together with the specification's numerical limits.
Use the clean calculator URL to cite the tool. A shared input link identifies a particular calculation. Content updates, numerical verification, and domain reviews have separate dates and scopes.
Input replay and numerical range
All entries share math.linear_algebra v1.14.16. Enter every cell as a finite decimal, including scientific notation. Empty cells and nonzero decimals below the binary64 range are rejected. QR, SVD, and least squares accept dimensions from 2 to 4; other operations require square matrices. Matching aliases may be supplied, while conflicting aliases or nested and flat cells are rejected.
A singular system may be consistent with infinitely many solutions or inconsistent; read the status and warnings. Tolerance-based rank and subspace decisions are not exact symbolic certificates. Results outside the numerical range refuse with NUMERIC_PRECISION_LIMIT. An unavailable auxiliary determinant is omitted with a warning, rather than reported as zero or null.
Calculations run locally in the browser. Shared query links contain inputs, are sent to the server when opened, and may appear in server or third-party logs. Anyone receiving a shared link can read its inputs. JSON and CSV exports include the original inputs, complete structured result, warnings, and calculation version.
Related tools
Other calculators in this family: Cholesky Factorization Calculator, Column Space Calculator, Condition Number Calculator, Least Squares Calculator, Left Nullspace Calculator, LU Factorization Calculator, Matrix Determinant Calculator, Matrix Eigenvalues Calculator . Explore all Linear Algebra.
Frequently asked questions
Key distinctions behind the calculation.
Is this a computer-algebra system?
No. It is small real matrices (order ≤ 4).
Where is the workspace?
/workspace/matrix uses this same engine. Calculator URLs stay /calc/math/linear-system.