我正在寻找一个好的（在最好的情况下积极维护的）C++ 矩阵库。因此它应该被模板化，因为我想使用有理数复数作为数字类型。我处理的矩阵主要是稀疏矩阵和酉矩阵。
您能否建议图书馆并简要解释一下为什么要使用它们，因为我知道如何找到它们，但我无法真正决定什么适合我，因为我错过了使用它们的经验。
编辑：
我处理的主要操作是矩阵乘法、与向量的标量乘法和克罗内克积。矩阵的大小是指数级的，我希望至少能够处理高达 1024x1024 条目的矩阵。
I am looking for a good (in the best case actively maintained) C++ matrix library. Thereby it should be templated, because I want to use a complex of rationals as numerical type. The matrices what I am dealing with are mainly sparse and unitary.
Can you please suggest libraries and also give a small explaination why to use them, because I know how to find them, but I cannot really decide what is suitable for me because I am missing the experience with them.
EDIT:
The main operations I am dealing with are matrix multiplication, scalar multiplication with a vector and kronecker product. The size of the matrices is exponential and I wanna at least be able to deal with matrices up to 1024x1024 entries.
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许多人做“严肃的”矩阵工作，依赖 BLAS，添加 LAPACK / ATLAS （正规矩阵）或 UMFPACK （稀疏矩阵）更高级的数学。原因是该代码经过充分测试、稳定、可靠且速度相当快。此外，您可以直接从供应商处购买它们（例如Intel MKL）调整您的架构，但也可以免费获得它们。 uBLAS 在 Manuel的答案可能是标准的C++ BLAS实现。如果您稍后需要 LAPACK 之类的东西，可以使用 绑定 来实现。
然而，这些标准库（BLAS / LAPACK / ATLAS 或 uBLAS + 绑定 + LAPACK / ATLAS）都无法满足您的模板化和易于使用的要求（除非 uBLAS 是您所需要的）。实际上，我必须承认，当我使用 BLAS / LAPACK 实现时，我倾向于直接调用 C / Fortran 接口，因为我通常在 uBLAS + 绑定组合中看不到太多额外的优势。
如果我需要一个简单易用的通用 C++ 矩阵库，我倾向于使用 Eigen（我过去曾经使用NewMat）。优点：
缺点（IMO）：
编辑：即将推出的 Eigen 3.1 允许某些函数使用 Intel MKL（或任何其他 BLAS / LAPACK 实现）。
Many people doing "serious" matrix stuff, rely on BLAS, adding LAPACK / ATLAS (normal matrices) or UMFPACK (sparse matrices) for more advanced math. The reason is that this code is well-tested, stable, reliable, and quite fast. Furthermore, you can buy them directly from a vendor (e.g. Intel MKL) tuned towards your architecture, but also get them for free. uBLAS mentioned in Manuel's answer is probably the standard C++ BLAS implementation. And if you need something like LAPACK later on, there are bindings to do so.
However, none of these standard libraries (BLAS / LAPACK / ATLAS or uBLAS + bindings + LAPACK / ATLAS) ticks your box for being templated and easy to use (unless uBLAS is all you'll ever need). Actually, I must admit, that I tend to call the C / Fortran interface directly when I use a BLAS / LAPACK implementation, since I often don't see much additional advantage in the uBLAS + bindings combination.
If I a need a simple-to-use, general-purpose C++ matrix library, I tend to use Eigen (I used to use NewMat in the past). Advantages:
Disadvantages (IMO):
Edit: The upcoming Eigen 3.1 allows some functions to use the Intel MKL (or any other BLAS / LAPACK implementation).
Boost uBLAS，因为它通过了升压滤波器。
有一些支持稀疏矩阵的模板库，因此如果您不更具体地了解您的需求，那么真的很难提出更好的理由。
Boost uBLAS, because it's passed the Boost filter.
There are a few template libs that support sparse matrices, so it's really hard to come up with a better rationale if you're not more specific about your needs.
您还应该尝试 MLT 和 HASEM 矩阵 C++ 库。最后一个有很好的记录。
You should also try MLT and HASEM Matrix C++ Library. The last one is very well documented.