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OpenBLAS Low-Rank Matrix Compression (SVD/PCA)
x402
OpenBLAS Low-Rank Matrix Compression (SVD/PCA) is a paid API for AI agents from stat.halowerk.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-29).
Factorizes a matrix into rank-k left and right singular vectors with singular values, optionally centering columns for PCA, and returns reconstruction error and spectral energy retained along with the full spectrum.
Price / call$0.002
Success—
StatusUnknown
How this service works
Factors a matrix into rank-k left and right factors with their singular values, optionally centering the columns first, and states what the compression actually cost: the Frobenius reconstruction error against the input and the fraction of spectral energy retained. The full spectrum comes back too, so a caller can pick a different k without a second call. With centering enabled this is principal component analysis, and the column means are returned as part of the model.
Output
Returns left singular vectors (U), right singular vectors (V), and singular values for the requested rank k, plus the full singular value spectrum so the caller can re-evaluate without a second call. Also reports the Frobenius reconstruction error between the low-rank approximation and the original matrix, the fraction of total spectral energy retained, and (when center_columns is true) the column means used during centering.
Example use cases
PCA on customer feature matrix
“I have a 100×20 matrix of customer features and I want to run PCA on it — center the columns and reduce it to rank 4. Give me the principal components, singular values, and tell me what fraction of the variance is retained.”
Choosing optimal rank for image compression
“I've got a 128×128 grayscale image flattened into a numeric matrix and I want to try compressing it to rank 10. Don't center the columns — just give me the SVD factors, the Frobenius error, and the full spectrum so I can evaluate whether rank 10 is enough.”
Latent factor extraction for recommendation data
“Take this 80×50 user-item interaction matrix and factorize it down to rank 8 without centering. I need the left and right factor matrices and the singular values for a latent factor model, plus the reconstruction error to validate quality.”
Technical details
Endpoint
POSThttps://stat.halowerk.com/v1/oss-openblas-low-rank-matrix-compress?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need truncated SVD or PCA on a matrix up to 128×128 and want reconstruction diagnostics (Frobenius error, spectral energy fraction) and the full spectrum in a single call, avoiding a second round-trip to pick a different rank. Prefer it over generic linear algebra libraries when you need a hosted, pay-per-call solution with no infrastructure overhead and OpenBLAS-backed numerical precision.
Side effects
- rank k exceeds the smaller matrix dimension — returns an error
- matrix dimensions exceed 128×128 limit — returns validation error
- matrix contains values outside the allowed ±1,000,000 range — returns validation error
- matrix has inconsistent row lengths (ragged array) — returns parsing error
- payment not included or insufficient — returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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