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Numora QR Decomposition API
x402
Numora QR Decomposition API is a paid API for AI agents from numomo.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-15).
Computes the QR decomposition of a matrix, returning orthogonal (Q) and upper-triangular (R) factor matrices
Price / call$0.1
Success—
StatusUnknown
How this service works
100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.
Output
Returns a JSON object with success=true, a result object containing the Q (orthogonal) and R (upper-triangular) factor matrices, and a human-readable computation description string explaining what was computed.
Example use cases
Least squares regression fitting
“I have an overdetermined system I need to solve — can you QR-decompose this coefficient matrix [[1,1],[1,2],[1,3],[1,4]] so I can use it for least squares fitting?”
Orthogonalization of basis vectors
“I need to orthogonalize these basis vectors using QR decomposition — the matrix is [[3,1],[4,1],[5,9],[2,6]]. Can you factor it into Q and R?”
Numerical stability check for matrix
“Can you QR-decompose my 3x3 matrix [[2,1,0],[1,3,1],[0,1,2]] so I can inspect the R factor for near-zero diagonal entries that would indicate rank deficiency?”
Technical details
Endpoint
POSThttps://numomo.vercel.app/api/matrix/qrWhen to prefer
Choose this endpoint when you need a pure, dependency-free QR matrix factorization without setting up a local numerical library. Ideal for AI agents performing on-the-fly linear algebra as part of a larger workflow — e.g. solving least squares problems, orthogonalizing basis sets, or preprocessing matrices for eigenvalue computations — where spinning up Python/numpy is not viable. Priced per-call at $0.1 USDC via x402 micropayments on Base, suitable for occasional agent use rather than high-volume batch jobs.
Side effects
- Non-rectangular or malformed 2D array returns an error
- Empty matrix input causes failure
- Non-numeric values in matrix entries cause computation error
- Singular or rank-deficient matrices may produce degenerate results
- Missing required field A returns a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| Arequired | array | Matrix (2D array) |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| resultrequired | object | Computation result varies by endpoint |
| successrequired | boolean | Always true on success |
| computationrequired | string | Human-readable description of what was computed |
Tags
x402
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