线性代数-SVD在C/C++中求解harwell-boeing稀疏a.x=b系统

linear algebra - SVD to solve harwell-boeing sparse a.x=b system in C/C++?

本文关键字:稀疏 harwell-boeing 系统 -SVD C++ 线性代数      更新时间:2023-10-16

有人知道c++的稀疏SVD求解器吗?我的问题涉及一些条件恶劣的矩阵,这些矩阵可能将列/行归零。我的数据存储在uBLAS矩阵中,该矩阵是Harwell Boeing稀疏格式。

我在查找时遇到了一些问题

SVD求解器

  1. 可以对稀疏矩阵进行操作的SVD解算器。拉帕克好像做不到?我想要将稀疏矩阵传递给函数并输出稀疏矩阵
  2. 一种重新组合结果的方法。。。这样我就可以从x=b(A^-1(中读出xs。我希望这是x=(b((v.(d^-1(.(u^t((

我希望从GSL 中重新创建以下两个步骤

gsl_linalg_SV_decomp_jacobi (gsl_matrix * A, gsl_matrix * V, gsl_vector * S) 
gsl_linalg_SV_solve (const gsl_matrix * U, const gsl_matrix * V, const gsl_vector * S, const gsl_vector * b, gsl_vector * x)

我也不知道如何用c++封装FORTRAN库。哪里/有任何PROPACK c/c++绑定吗?

编辑1:我在使用PROPACK时遇到了一些问题。PROPACK输出稀疏矩阵吗?它似乎将V输出为"V(LDV,KMAX(:DOUBLE PRECISION array"。这意味着它没有?

SVDLIBC是一个C库,部分支持Harwell Boeing格式。我对这个图书馆不熟悉,但从表面上看,它似乎符合你的要求。

您提到了PROPACK。Fortran是C兼容的,您只需要知道调用约定是如何工作的。我不确定,但我认为您想在PROPACK中调用的函数是dlansvd(假设为双精度(,其文档如下:

  subroutine dlansvd(jobu,jobv,m,n,k,kmax,aprod,U,ldu,Sigma,bnd,
 c     V,ldv,tolin,work,lwork,iwork,liwork,doption,ioption,info,
 c     dparm,iparm)

c     DLANSVD: Compute the leading singular triplets of a large and
c     sparse matrix by Lanczos bidiagonalization with partial
c     reorthogonalization.
c
c     Parameters:
c
c     JOBU: CHARACTER*1. If JOBU.EQ.'Y' then compute the left singular vectors.
c     JOBV: CHARACTER*1. If JOBV.EQ.'Y' then compute the right singular 
c           vectors.
c     M: INTEGER. Number of rows of A.
c     N: INTEGER. Number of columns of A.
c     K: INTEGER. Number of desired singular triplets. K <= MIN(KMAX,M,N)
c     KMAX: INTEGER. Maximal number of iterations = maximal dimension of
c           the generated Krylov subspace.
c     APROD: Subroutine defining the linear operator A. 
c            APROD should be of the form:
c
c           SUBROUTINE DAPROD(TRANSA,M,N,X,Y,DPARM,IPARM)
c           CHARACTER*1 TRANSA
c           INTEGER M,N,IPARM(*)
c           DOUBLE PRECISION X(*),Y(*),DPARM(*)
c
c           If TRANSA.EQ.'N' then the function should compute the matrix-vector
c           product Y = A * X.
c           If TRANSA.EQ.'T' then the function should compute the matrix-vector
c           product Y = A^T * X.
c           The arrays IPARM and DPARM are a means to pass user supplied
c           data to APROD without the use of common blocks.
c     U(LDU,KMAX+1): DOUBLE PRECISION array. On return the first K columns of U
c               will contain approximations to the left singular vectors 
c               corresponding to the K largest singular values of A.
c               On entry the first column of U contains the starting vector
c               for the Lanczos bidiagonalization. A random starting vector
c               is used if U is zero.
c     LDU: INTEGER. Leading dimension of the array U. LDU >= M.
c     SIGMA(K): DOUBLE PRECISION array. On return Sigma contains approximation
c               to the K largest singular values of A.
c     BND(K)  : DOUBLE PRECISION array. Error estimates on the computed 
c               singular values. The computed SIGMA(I) is within BND(I)
c               of a singular value of A.
c     V(LDV,KMAX): DOUBLE PRECISION array. On return the first K columns of V
c               will contain approximations to the right singular vectors 
c               corresponding to the K largest singular values of A.
c     LDV: INTEGER. Leading dimension of the array V. LDV >= N.
c     TOLIN: DOUBLE PRECISION. Desired relative accuracy of computed singular 
c            values. The error of SIGMA(I) is approximately 
c            MAX( 16*EPS*SIGMA(1), TOLIN*SIGMA(I) )
c     WORK(LWORK): DOUBLE PRECISION array. Workspace of dimension LWORK.
c     LWORK: INTEGER. Dimension of WORK.
c            If JOBU.EQ.'N' and JOBV.EQ.'N' then  LWORK should be at least
c            M + N + 9*KMAX + 2*KMAX**2 + 4 + MAX(M+N,4*KMAX+4).
c            If JOBU.EQ.'Y' or JOBV.EQ.'Y' then LWORK should be at least
c            M + N + 9*KMAX + 5*KMAX**2 + 4 + 
c            MAX(3*KMAX**2+4*KMAX+4, NB*MAX(M,N)), where NB>1 is a block 
c            size, which determines how large a fraction of the work in
c            setting up the singular vectors is done using fast BLAS-3 
c            operation. 
c     IWORK: INTEGER array. Integer workspace of dimension LIWORK.
c     LIWORK: INTEGER. Dimension of IWORK. Should be at least 8*KMAX if
c             JOBU.EQ.'Y' or JOBV.EQ.'Y' and at least 2*KMAX+1 otherwise.
c     DOPTION: DOUBLE PRECISION array. Parameters for LANBPRO.
c        doption(1) = delta. Level of orthogonality to maintain among
c          Lanczos vectors.
c        doption(2) = eta. During reorthogonalization, all vectors with
c          with components larger than eta along the latest Lanczos vector
c          will be purged.
c        doption(3) = anorm. Estimate of || A ||.
c     IOPTION: INTEGER array. Parameters for LANBPRO.
c        ioption(1) = CGS.  If CGS.EQ.1 then reorthogonalization is done
c          using iterated classical GRAM-SCHMIDT. IF CGS.EQ.0 then 
c          reorthogonalization is done using iterated modified Gram-Schmidt.
c        ioption(2) = ELR. If ELR.EQ.1 then extended local orthogonality is
c          enforced among u_{k}, u_{k+1} and v_{k} and v_{k+1} respectively.
c     INFO: INTEGER. 
c         INFO = 0  : The K largest singular triplets were computed succesfully
c         INFO = J>0, J<K: An invariant subspace of dimension J was found.
c         INFO = -1 : K singular triplets did not converge within KMAX
c                     iterations.   
c     DPARM: DOUBLE PRECISION array. Array used for passing data to the APROD
c         function.   
c     IPARM: INTEGER array. Array used for passing data to the APROD
c         function.   
c
c     (C) Rasmus Munk Larsen, Stanford, 1999, 2004 
c

在Fortran中,需要记住的重要事项是,所有参数都通过引用传递,并且非稀疏数组以列主格式存储。因此,这个函数在C++中的正确声明应该如下(未经测试(:

extern "C"
void dlansvd(const char *jobu,
             const char *jobv,
             int *m,
             int *n,
             int *k,
             int *kmax,
             void (*aprod)(const char *transa,
                           int *m,
                           int *n,
                           int *iparm,
                           double *x,
                           double *y,
                           double *dparm),
             double *U,
             int *ldu,
             double *Sigma,
             double *bnd,
             double *V,
             int *ldv,
             double *tolin,
             double *work,
             int *lwork,
             int *iwork,
             int *liwork,
             double *doption,
             int *ioption,
             int *info,
             double *dparm,
             int *iparm);

这真是一头野兽。祝你好运

不妨看看Tim Davis的稀疏线性代数软件:http://www.cise.ufl.edu/~戴维斯/

总的来说,我发现他的软件非常有用,通常非常高效和健壮。

他似乎一直在和一个学生一起做一个稀疏的SVD包,但我不确定这个项目处于什么阶段

希望这能有所帮助。