Matrix Rank Calculator
Rank of a small real matrix via Gaussian elimination. Same engine as Linear System. 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
- rank(A) for order 2–3 (engine up to 4).
- 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
- 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
Enter a square matrix
2×2 or 3×3. Larger composition lives at /workspace/matrix.
Read rank(A)
rank < n means singular: inverse does not exist and det = 0.
Example calculations
Common configurations with formula and result.
Dependent rows
[[1,2],[2,4]]
Identity
I₂
Matrix Rank 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
- rank(A) is the number of linearly independent rows (or columns). This page is a discovery surface on the linear-algebra engine (mode=rank). Determinant, inverse, and Ax=b share the same capability.
- What it calculates
- rank(A) for order 2–3 (engine up to 4).
- Inputs
- order
- a11…
- Outputs
- rank
- det
- Formula
rank via Gaussian elimination- Assumptions
- Real matrices
- Order ≤ 4
- Planar ℝ² figure when order=2; order 3+ omits it
- Not a CAS
- Not SVD
- Units
- dimensionless
- Boundary conditions
- unknown mode (including jordan-form) → INVALID_MODE
- Example
- [[1,2],[2,4]] → rank=1
- Validation cases
2 published on this page · 58/58 tests · Production surface contract 6/6 · View evidence
- order=2 a11=1 a12=2 a21=2 a22=4 → rank=1
- order=2 a11=1 a12=0 a21=0 a22=1 → rank=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.
- NIST DLMF §3.2 — Linear Algebra — §3.2Supports: Gaussian elimination, LU decomposition, conditioning, eigenvalues, and eigenvectors.
- ISO 80000-2:2019 Quantities and units — Mathematics
- Calculation version
- 1.14.16
Background
Interpretation and common distinctions.
What does this matrix rank calculator compute?
rank(A) is the number of linearly independent rows (or columns).
The published formula or method is rank via Gaussian elimination. The following references support the stated mathematics or method:
- NIST DLMF §3.2 — Linear Algebra: Gaussian elimination, LU decomposition, conditioning, eigenvalues, and eigenvectors.
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 [[1,2],[2,4]] with 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, Linear System Calculator, LU Factorization Calculator, Matrix Determinant Calculator . Explore all Linear Algebra.
Frequently asked questions
Key distinctions behind the calculation.
Is this a second rank algorithm?
No. It is mode=rank on math.linear_algebra, the same engine as /calc/math/linear-system and /workspace/matrix.
Is rank the same as the number of nonzero eigenvalues?
Not always (defective matrices). Eigenvalues are mode=eigen on this same engine; SVD stays out of the seed.