稀疏最小二乘回归工具
我想用几十万个例子进行稀疏高维（几千个特征）最小二乘回归。 我很高兴使用非花哨的优化 - 随机梯度下降就很好。
有谁知道已经实现了执行此操作的任何软件，这样我就不必编写自己的软件了？
亲切的问候。
I want to do sparse high dimensional (a few thousand features) least squares regression with a few hundred thousands of examples. I'm happy to use non fancy optimisation - stochastic gradient descent is fine.
Does anyone know of any software already implemented for doing this, so I don't have to write to my own?
Kind regards.
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虽然我不确定，但这让我觉得 LAPACK （线性algebra package）就能提供支持。 他们通常对大型矩阵数学感兴趣，包括稀疏矩阵和核外大小。 基本版本是 FORTRAN，但也有 C 和其他语言的库的移植。
由于 LAPACK 使用 BLAS（基本线性代数子程序）进行许多底层调用，您可能还想查看 稀疏 BLAS。
While I don't know for sure, this strikes me as the kind of thing that LAPACK (linear algebra package) would be able to provide support for. They are typically interested in large matrix math, incluing sparse matrices and out-of-core sizes. The basic version is FORTRAN, but there are ports of the libraries for C and other languages.
As LAPACK uses BLAS (basic linear algebra subprograms) for many of its underlying calls, you will probably also want to check out Sparse BLAS.
我非常确定 R 包 可以用于解决此类问题。 它非常强大且灵活。 该页面链接了许多在线资源。
I'm pretty sure that the R package can be used for problems like this. It's incredibly powerful and flexible. Lots of online resources linked from that page.
我建议看看 LAPACK。 它是一个相当成熟的线性代数库，尽管与它的交互可能有点棘手，因为它是用 Fortran 编写的。 不过，这很好，因为 Fortran 与 C 的 ABI 兼容，如果你的函数原型正确的话。
[编辑] 经过进一步审查，LAPACK 似乎不支持稀疏矩阵。 它可以出于某些目的处理带状矩阵，但对于线性最小二乘问题，它仅支持一般矩阵。
I'd suggest taking a look at LAPACK. It's a pretty mature linear algebra library, although interfacing with it can be a little tricky, since it's written in Fortran. That's fine, though, since Fortran is ABI compatible with C, if you get your function prototypes right.
[Edit] Upon further review, it appears that LAPACK does not support sparse matrices. It can handle banded matrices for some purposes, but for the linear least-squares problem, it only supports general matrices.