Arpack是一个优秀的大型稀疏矩阵特征值求解开源库,其基于blas和lapack提供了高效的特征值求解方案。其他还有勉强的可替代方案如mkl,SLEPc,Spectra,Eigen等,这些库也提供了特征值求解功能,但是通常在效率和可并行性上不如arpack。
Arpack基于 Arnoldi 方法从一个给定的稀疏矩阵中提取部分特征值和特征向量。Arnoldi 方法通过逐步构造一个正交基,并在每次迭代时逼近矩阵的特征空间,从而实现高效的特征计算。调用Arpack可以通过C语言或者Fortran语言进行。
现在通常使用的版本叫Arpack-ng,这个版本在github上的地址为:
https://github.com/opencollab/arpack-ng
在之前的文章现代先进构建工具Cmake-使用Cmake构建C++和Fortran中, 我们介绍了采用Cmake构建C++和Fortran工程,实际上,对于这里的Arpack库,我们的编译过程就主要通过Cmake配合相应的编译器完成。
本文主要介绍在Windows下如何编译这个库,生成.lib静态文件,从而可以实现在我们自己的代码中使用arpack库。
编译过程:
安装Cmake;








/** This example demonstrates the use of C++ bindings to call arpack.** Use arpack as you would have normally done, but, use [ae]upd instead* of *[ae]upd_. The main advantage is that compiler checks the argument types* and the correct function is called based on the type (float vs double vs* complex). Note: to debug arpack, call debug_c. This is a test program to* solve for the 9 eigenvalues of A*x = lambda*x where A is the diagonal* matrix with entries 1000, 999, ... , 2, 1 on the diagonal.*/#include <cmath>#include <iostream>#include <vector>#include "arpack.hpp"#include "debug_c.hpp" // debug arpack.#include "stat_c.hpp" // arpack statistics.template <typename Real>void diagonal_matrix_vector_product(const Real* x, Real* y) {for (int i = 0; i < 1000; ++i) {y[i] = static_cast<Real>(i + 1) * x[i];}}template <typename Real>void real_symmetric_runner(double const& tol_check, arpack::which const& ritz_option) {const a_int N = 1000;const a_int nev = 9;const a_int ncv = 2 * nev + 1;const a_int ldv = N;const a_int ldz = N;const a_int lworkl = ncv * (ncv + 8);const a_int rvec = 1; // need eigenvectorsconst Real tol = 0.000001; // small tol => more stable checks after EV computation.const Real sigma = 0.0; // not referenced in this modestd::vector<Real> resid(N);std::vector<Real> V(ldv * ncv);std::vector<Real> z(ldz * nev);std::vector<Real> d(nev);std::vector<Real> workd(3 * N);std::vector<Real> workl(lworkl);std::vector<a_int> select(ncv); // since HOWMNY = 'A', only used as workspace herea_int iparam[11], ipntr[11];iparam[0] = 1; // ishiftiparam[2] = 10 * N; // on input: maxit; on output: actual iterationiparam[3] = 1; // NB, only 1 allowediparam[6] = 1; // modea_int info = 0, ido = 0;do {arpack::saupd(ido, arpack::bmat::identity, N,ritz_option, nev, tol, resid.data(), ncv,V.data(), ldv, iparam, ipntr, workd.data(),workl.data(), lworkl, info);diagonal_matrix_vector_product(&(workd[ipntr[0] - 1]), &(workd[ipntr[1] - 1]));} while (ido == 1 || ido == -1);// check info and number of ev found by arpack.if (info < 0 || iparam[4] < nev) { /*arpack may succeed to compute more EV than expected*/std::cout << "ERROR in saupd: iparam[4] " << iparam[4] << ", nev " << nev<< ", info " << info << std::endl;throw std::domain_error("Error inside ARPACK routines");}arpack::seupd(rvec, arpack::howmny::ritz_vectors, select.data(), d.data(),z.data(), ldz, sigma, arpack::bmat::identity, N,ritz_option, nev, tol, resid.data(), ncv,V.data(), ldv, iparam, ipntr, workd.data(),workl.data(), lworkl, info);if (info < 0) throw std::runtime_error("Error in seupd, info " + std::to_string(info));for (int i = 0; i < nev; ++i) {Real val = d[i];Real ref = static_cast<Real>(N - (nev - 1) + i);Real eps = std::fabs(val - ref);std::cout << val << " - " << ref << " - " << eps << std::endl;/*eigen value order: smallest -> biggest*/if (eps > tol_check) throw std::domain_error("Correct eigenvalues not computed");}std::cout << "------" << std::endl;}template <typename Real>void diagonal_matrix_vector_product(const std::complex<Real>* x, std::complex<Real>* y) {for (int i = 0; i < 1000; ++i) {// Use complex matrix (i, -i) instead of (i, i): this way "largest_magnitude"// and "largest_imaginary" options produce different results that can be checked.y[i] = x[i] * std::complex<Real>{Real(i + 1), -Real(i + 1)};}}template <typename Real>void complex_nonsymmetric_runner(double const& tol_check, arpack::which const& ritz_option) {const a_int N = 1000;const a_int nev = 9;const a_int ncv = 2 * nev + 1;const a_int ldv = N;const a_int ldz = N;const a_int lworkl = ncv * (3 * ncv + 5);const a_int rvec = 0; // eigenvectors omittedconst Real tol = 0.000001; // small tol => more stable checks after EV computation.const std::complex<Real> sigma(0.0, 0.0); // not referenced in this modestd::vector<std::complex<Real>> resid(N);std::vector<std::complex<Real>> V(ldv * ncv);std::vector<std::complex<Real>> z(ldz * nev);std::vector<std::complex<Real>> d(nev);std::vector<std::complex<Real>> workd(3 * N);std::vector<std::complex<Real>> workl(lworkl);std::vector<std::complex<Real>> workev(2 * ncv);std::vector<Real> rwork(ncv);std::vector<a_int> select(ncv); // since HOWMNY = 'A', only used as workspace herea_int iparam[11], ipntr[14];iparam[0] = 1; // ishiftiparam[2] = 10 * N; // on input: maxit; on output: actual iterationiparam[3] = 1; // NB, only 1 allowediparam[6] = 1; // modea_int info = 0, ido = 0;do {arpack::naupd(ido, arpack::bmat::identity, N,ritz_option, nev, tol, resid.data(), ncv,V.data(), ldv, iparam, ipntr, workd.data(),workl.data(), lworkl, rwork.data(), info);diagonal_matrix_vector_product(&(workd[ipntr[0] - 1]), &(workd[ipntr[1] - 1]));} while (ido == 1 || ido == -1);// check info and number of ev found by arpack.if (info < 0 || iparam[4] < nev) { /*arpack may succeed to compute more EV than expected*/std::cout << "ERROR in naupd: iparam[4] " << iparam[4] << ", nev " << nev<< ", info " << info << std::endl;throw std::domain_error("Error inside ARPACK routines");}arpack::neupd(rvec, arpack::howmny::ritz_vectors, select.data(), d.data(),z.data(), ldz, sigma, workev.data(), arpack::bmat::identity, N,ritz_option, nev, tol, resid.data(), ncv,V.data(), ldv, iparam, ipntr, workd.data(),workl.data(), lworkl, rwork.data(), info);if (info < 0) throw std::runtime_error("Error in neupd, info " + std::to_string(info));if (ritz_option == arpack::which::largest_magnitude) {for (int i = 0; i < nev; ++i) {Real rval = std::real(d[i]);Real rref = static_cast<Real>(N - (nev - 1) + i);Real reps = std::fabs(rval - rref);Real ival = std::imag(d[i]);Real iref = -static_cast<Real>(N - (nev - 1) + i);Real ieps = std::fabs(ival - iref);std::cout << rval << " " << ival << " - " << rref << " " << iref << " - " << reps << " " << ieps << std::endl;if (reps > tol_check || ieps > tol_check) throw std::domain_error("Correct eigenvalues not computed");}}else if (ritz_option == arpack::which::largest_imaginary) {for (int i = 0; i < nev; ++i) {Real rval = std::real(d[i]);Real rref = static_cast<Real>(nev - i);Real reps = std::fabs(rval - rref);Real ival = std::imag(d[i]);Real iref = -static_cast<Real>(nev - i);Real ieps = std::fabs(ival - iref);std::cout << rval << " " << ival << " - " << rref << " " << iref << " - " << reps << " " << ieps << std::endl;if (reps > tol_check || ieps > tol_check) throw std::domain_error("Correct eigenvalues not computed");}}else {throw std::domain_error("The input Ritz option is not allowed in this test file.");}std::cout << "------" << std::endl;}int main() {sstats_c();// arpack without debugreal_symmetric_runner<float>(1., arpack::which::largest_magnitude);real_symmetric_runner<float>(1., arpack::which::largest_algebraic);real_symmetric_runner<double>(1.e-05, arpack::which::largest_magnitude);real_symmetric_runner<double>(1.e-05, arpack::which::largest_algebraic);a_int nopx_c, nbx_c, nrorth_c, nitref_c, nrstrt_c;float tsaupd_c, tsaup2_c, tsaitr_c, tseigt_c, tsgets_c, tsapps_c, tsconv_c;float tnaupd_c, tnaup2_c, tnaitr_c, tneigt_c, tngets_c, tnapps_c, tnconv_c;float tcaupd_c, tcaup2_c, tcaitr_c, tceigt_c, tcgets_c, tcapps_c, tcconv_c;float tmvopx_c, tmvbx_c, tgetv0_c, titref_c, trvec_c;stat_c(nopx_c, nbx_c, nrorth_c, nitref_c, nrstrt_c, tsaupd_c, tsaup2_c,tsaitr_c, tseigt_c, tsgets_c, tsapps_c, tsconv_c, tnaupd_c, tnaup2_c,tnaitr_c, tneigt_c, tngets_c, tnapps_c, tnconv_c, tcaupd_c, tcaup2_c,tcaitr_c, tceigt_c, tcgets_c, tcapps_c, tcconv_c, tmvopx_c, tmvbx_c,tgetv0_c, titref_c, trvec_c);std::cout << "Timers : nopx " << nopx_c << ", tmvopx " << tmvopx_c;std::cout << " - nbx " << nbx_c << ", tmvbx " << tmvbx_c << std::endl;std::cout << "------" << std::endl;// set debug flagsdebug_c(6, -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1);// arpack with debugcomplex_nonsymmetric_runner<float>(1., arpack::which::largest_magnitude);complex_nonsymmetric_runner<float>(1., arpack::which::largest_imaginary);complex_nonsymmetric_runner<double>(1.e-05, arpack::which::largest_magnitude);complex_nonsymmetric_runner<double>(1.e-05, arpack::which::largest_imaginary);return 0;}





以上,即是Windows下采用Cmake构建编译大型开源稀疏矩阵特征值求解库Arpack-ng的过程。实践表明,这个库具有良好的性能,具体的使用,主要是dsaupd和dseupd这两个函数,但是具体参数很多,并且求解广义特征值时可能需要搭配一个求解线性方程组的求解库和高效的矩阵向量乘函数,这部分可以配合mkl的pardiso,mumps等高效的线性方程组求解库完成。
以上,即是本文的全部内容,感谢阅读。
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