{"uid":"cap_yTf3GIrF93W5BBqNT6u14","slug":"numora-matrix-eigenvectors-api-da1b79e7","name":"Numora Matrix Eigenvectors API","description":"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.","url":"https://numomo.vercel.app/api/matrix/eigenvectors","method":"POST","headers":{},"bodySchema":{"type":"object","required":["A"],"properties":{"A":{"type":"array","description":"Matrix (2D array)"}}},"responseSchema":{"type":"object","required":["success","result","computation"],"properties":{"result":{"type":"object","description":"Computation result varies by endpoint"},"success":{"type":"boolean","description":"Always true on success"},"computation":{"type":"string","description":"Human-readable description of what was computed"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_u6kFrgp-flrqJu1aHjESI","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes the eigenvalues and eigenvectors of a given square matrix using pure numerical methods.","exampleAgentPrompt":"Can you compute the eigenvectors and eigenvalues of this matrix: [[4, 1], [2, 3]]?","exampleUseCases":[{"title":"Principal Component Analysis basis","prompt":"I need the eigenvectors of my covariance matrix [[2.5, 2.4], [2.4, 2.4], [0.49, 0.83], [0.83, 1.21]] — wait, it's actually [[2.5, 0.49], [0.49, 1.21]] — can you find the principal axes?"},{"title":"Structural engineering modal analysis","prompt":"I have this stiffness matrix for a 3-DOF system: [[6, -2, 0], [-2, 4, -2], [0, -2, 2]] — can you compute its eigenvalues and eigenvectors so I can identify the natural vibration modes?"},{"title":"Graph Laplacian spectral decomposition","prompt":"My graph Laplacian matrix is [[2, -1, -1], [-1, 1, 0], [-1, 0, 1]]. Compute the eigenvectors so I can do spectral clustering on it."}],"resultDescription":"Returns a JSON object with 'success' (boolean), 'result' (an object containing arrays of eigenvalues and corresponding eigenvectors of the input matrix), and 'computation' (a human-readable string describing what was computed). Eigenvalues may be real or complex depending on the input matrix.","failureModes":["Non-square matrix input — eigenvectors are only defined for square matrices","Singular or degenerate matrix may produce numerical instability in results","Malformed or non-numeric array input returns an error","Very large matrices may timeout or exceed computation limits","Complex eigenvalues returned as real/imaginary pairs may require client-side handling"],"whenToPreferThis":"Choose this endpoint when you need pure server-side eigendecomposition with no external library dependencies, especially in agent pipelines where lightweight micropayment-based math calls are preferred over spinning up a full scientific computing environment. Ideal for on-demand linear algebra tasks without provisioning numpy/scipy infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:46:17.950Z","isFirstParty":false}