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Numora Matrix QR Decomposition
x402
Numora Matrix QR Decomposition is a paid API for AI agents from numormor.netlify.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).
Computes the QR decomposition of a given 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' (boolean), 'result' containing the Q (orthogonal) and R (upper-triangular) factor matrices as 2D arrays, and 'computation' as a human-readable description of the operation performed.
Example use cases
Least-squares regression analysis engine
“I have a dataset with multiple features and need to fit a least-squares regression model. Can you break down my feature matrix using QR decomposition so we can solve for the best-fit coefficients without numerical instability?”
Linear system solver for robotics
“My robot's motion planning needs to solve a system of linear equations for trajectory optimization. Use QR decomposition on my constraint matrix to give me stable Q and R factors we can use for efficient solving.”
Eigenvalue computation preprocessing
“I'm working on finding eigenvalues of a large rectangular matrix for data analysis. Decompose it into orthogonal Q and upper-triangular R factors first — that'll give us a cleaner matrix to work with for the next step.”
Technical details
Endpoint
POSThttps://numormor.netlify.app/api/matrix/qrWhen to prefer
Choose this endpoint when you need a pure, dependency-free server-side QR matrix decomposition — especially useful for AI agents that cannot run local linear algebra libraries, need reproducible server-computed factorizations for least-squares fitting, or require QR as a preprocessing step for solving linear systems or computing eigenvalues.
Side effects
- Non-numeric matrix elements cause a 400/422 validation error
- Non-rectangular or jagged array input returns an error
- Empty matrix or empty rows return an error
- Singular or ill-conditioned matrices may return degenerate Q/R factors
- Payment not provided or invalid x402 payment header returns 402
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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