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Numora Linear Programming Solver
x402
Numora Linear Programming Solver 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).
Solves linear programming optimization problems given objective coefficients and constraint 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 a 'success' boolean, a 'result' object containing the optimal variable values and the optimal objective function value, and a 'computation' string describing what was solved in human-readable form.
Example use cases
Production mix optimization
“I'm trying to figure out the cheapest way to produce two products. My objective coefficients are [-5, -4] for profit maximization, the constraint matrix A is [[1,1],[10,6]] representing labor and material limits, and the bounds b are [14, 100]. What's the optimal production mix?”
Diet problem nutrient minimization
“Help me find the minimum-cost diet: objective coefficients are [3, 24, 1.3] for three foods, the constraint matrix A is [[-60,-300,-10],[-10,-10,-300],[-1,-0,0]] for nutrient requirements, and b is [-70,-75,-0.5]. Solve this linear program for me.”
Logistics cost minimization
“I have a shipping cost vector c of [2,3,1,5] and a constraint matrix A with rows representing supply and demand limits, and bounds b of [100,150,80,120]. Minimize total shipping cost and tell me how much to ship along each route.”
Technical details
Endpoint
POSThttps://numomo.vercel.app/api/solve/linear-programmingWhen to prefer
Use this endpoint when you need to solve a standard linear programming problem (minimize or maximize a linear objective subject to linear inequality constraints) without setting up a local solver. It is ideal for resource allocation, production planning, diet problems, and logistics routing where all relationships are linear. Prefer this over general equation solvers when your problem has inequality constraints and a linear objective function.
Side effects
- Infeasible problem: constraints are contradictory and no solution exists
- Unbounded problem: objective can be minimized/maximized to infinity
- Malformed input: A, b, or c arrays have mismatched dimensions
- Empty or missing required fields (A, b, or c) returns a validation error
- Non-numeric values in input arrays cause parse errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| Arequired | array | |
| brequired | array | |
| crequired | array | Objective coefficients |
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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