{"uid":"cap_QIftUEg-ZvuJ4htsv9YF7","slug":"numora-matrix-qr-decomposition-c5c167ee","name":"Numora Matrix QR Decomposition","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://numormor.netlify.app/api/matrix/qr","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":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":{"p10Cents":"10.0000","medianCents":"10.0000","p90Cents":"10.0000","minCents":"10.0000","maxCents":"10.0000","p95Cents":"10.0000","sampleCount":1,"varies":false,"failureChargeRate":1},"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_0gqunwSzG5iMyaFNPi7J0","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes the QR decomposition of a given matrix, returning orthogonal (Q) and upper-triangular (R) factor matrices.","exampleAgentPrompt":"Perform a QR decomposition on this matrix: [[12, -51, 4], [6, 167, -68], [-4, 24, -41]] — give me the orthogonal Q matrix and upper-triangular R matrix.","exampleUseCases":[{"title":"Least-squares regression analysis engine","prompt":"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?"},{"title":"Linear system solver for robotics","prompt":"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."},{"title":"Eigenvalue computation preprocessing","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:49:22.385Z","isFirstParty":false}