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Numora Matrix Condition Number
x402
Numora Matrix Condition Number 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 condition number of a matrix, indicating how sensitive a linear system is to numerical errors or perturbations.
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 a boolean success flag, a result object containing the computed condition number (a scalar value indicating matrix conditioning — values close to 1 are well-conditioned, very large values indicate ill-conditioning or near-singularity), and a human-readable computation description string.
Example use cases
Numerical stability check before solving
“I'm about to solve a linear system Ax=b. Can you compute the condition number of my matrix A = [[10, 7, 8, 7], [7, 5, 6, 5], [8, 6, 10, 9], [7, 5, 9, 10]] to see if the system is well-conditioned?”
Machine learning feature matrix assessment
“I have a feature matrix [[1, 2, 3], [4, 5, 6], [7, 8, 10]] and I want to know its condition number to understand if multicollinearity might be a problem in my regression model.”
Engineering simulation matrix validation
“Before running my finite element simulation, can you check the condition number of this stiffness matrix [[200, -100, 0], [-100, 200, -100], [0, -100, 100]] to make sure it won't cause numerical issues?”
Technical details
Endpoint
POSThttps://numomo.vercel.app/api/matrix/condition-numberWhen to prefer
Choose this endpoint when you need a pure, dependency-free numerical computation of a matrix's condition number without setting up a local math library. Ideal for AI agents performing linear algebra checks in pipelines, especially when assessing whether a matrix is suitable for numerical methods like solving linear equations or inverting. Particularly useful in environments where installing scipy or numpy is impractical.
Side effects
- Non-square or singular matrix may return an error or infinity
- Malformed 2D array input (jagged rows, non-numeric values) causes validation failure
- Singular matrix (determinant zero) may yield infinite or undefined condition number
- Empty matrix or scalar input rejected as invalid
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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