lobpcg::TruncatedSvd maps non-converged LOBPCG results to Ok, hiding convergence failure
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評估
研究方向
Start in src/lobpcg/svd.rs at TruncatedSvd::decompose and inspect LobpcgResult handling around lines 162-172, then review the result type and the maxiter setup around line 105. Check src/generate.rs and issue #336 for the separate RNG concern. Done means non-convergence is no longer silently reported as success, with the chosen result API covered by appropriate tests.
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描述
Summary
lobpcg::TruncatedSvd::decompose maps a non-converged LOBPCG result onto Ok(..), so callers cannot distinguish a converged decomposition from a failed one and may silently receive wrong singular values.
Version inspected: ndarray-linalg 0.17.0 (source from crates.io).
Where
match res {
LobpcgResult::Ok(vals, vecs, _) | LobpcgResult::Err(vals, vecs, _, _) => {
Ok(TruncatedSvdResult {
problem: self.problem,
eigvals: vals,
eigvecs: vecs,
ngm: n > m,
})
}
LobpcgResult::NoResult(err) => Err(err),
}
LobpcgResult::Err(..) carries the error as its fourth field and it is dropped on the floor. Returning the partial result is reasonable; giving the caller no way to learn that it is partial is the problem.
Contributing factor: maxiter defaults to problem.len_of(Axis(0)) * 2 (svd.rs:105), which is a small iteration budget for small inputs — exactly where I measured the worst errors in the sibling implementation.
Evidence
I want to be straightforward about what I did and did not run.
Verified by source inspection in ndarray-linalg 0.17.0: the LobpcgResult::Err(..) => Ok(..) mapping above, and the maxiter default.
Not executed against ndarray-linalg: I do not have a LAPACK backend configured locally, so I could not run a reproduction through this crate.
Measured in linfa-linalg 0.2.1, whose lobpcg::TruncatedSvd is the same algorithm with the same structure (Err((_, Some(Lobpcg{..}))) => Ok(..), same 2 * n_samples default, same epsilon * correction * λ_max cutoff). Taking X = U diag(4, 3, 2, 1) Vᵀ with U (8×4) and V (4×4) built from Sylvester–Hadamard columns, so the singular values are exactly [4, 3, 2, 1] by construction:
k=1 returned Ok sigma=[4.0] max rel err=2.220e-16
k=2 returned Ok sigma=[4.0, 3.0] max rel err=4.441e-16
k=3 returned Ok sigma=[3.94368, 2.67522, 1.477172] max rel err=2.614e-1 <== WRONG
k=4 returned Ok sigma=[4.0, 3.0, 2.0, 1.0] max rel err=2.517e-15
A 26% error reported as success, with neighbouring block sizes exact to machine precision. I would expect the same behaviour here given the shared structure, but I have not confirmed the exact numbers under LAPACK — if that distinction matters to you, treat the code-level finding as the claim and the numbers as indicative.
Filed in parallel as rust-ml/linfa-linalg#20.
Secondary: the result is not reproducible
decompose initialises its starting block with generate::random(..) (svd.rs:132), and generate::random uses thread_rng() (src/generate.rs:36). There is no new_with_rng equivalent, so successive runs of the same program on the same input can land on different subspaces and different component signs. Combined with the silent non-convergence above, that makes the failure intermittent as well as quiet. This overlaps with #336.
Suggested remedies
- Surface convergence to the caller — e.g. a
converged: boolor the residual norms onTruncatedSvdResult— rather than discarding the error. - Or return
Errby default, with the partial result available through an explicit opt-in. - Consider a seedable entry point (
new_with_rng) so results are reproducible;linfa-linalghas one.
Glad to put up a PR if you have a preference on direction.
- 主要語言
- Rust
- 星號
- 452
- 分支
- 95
- PR 合併指標
- 30 天內沒有已合併 PR
環境準備
這個專案沒有提供開發容器、Dockerfile 或貢獻指南,環境需要你自己搭建:先看它的 README,通用步驟見我們的新手貢獻指南。
從這裡開始
- 先讀完整個 Issue,再讀專案的貢獻指南。
- 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
- Fork 儲存庫,在一個分支上完成修改。
- 送出 Pull Request,並在描述裡引用這個 Issue 編號。
rust-ndarray/ndarray-linalg 的其他 Issue
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Thin SVD未關閉
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rust-ndarray/ndarray-linalg#401 · 2 個 reaction ·
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