在本論文中,我們將有系統的調查兩種用於解決二次特徵值問題(QEPs)的eigenpackage, 包含：the Scalable Library for Eigenvalue Problem Computations(SLEPc)與ParallelJacobi-Davidson Package(PJDPack)這兩個建構在Portable, Extensible Toolkit for Scientific Computation(PETSc)的Package. 對於這兩個eigenpackage最主要的差別在於SLEPc是使用linearization approach並且有多種不同的eigensolver去解決generalized後的特徵值問題. 而另一方面, PJDPack只有使用PJD演算法並且對於二次特徵值問題是使用直接解法. 為了能夠進行接下來的討論, 我們使用一個Matlab-based的工具, a collection of nonlinear eigenvalue problem (NLEVP)來製作大量具有差異性值的矩陣來做一些數值實驗並且用robustness, accuracy和efficiency來評估效率問題.In this thesis, we systematically investigate the numerical performance of two eigenpackages for solving quadratic eigenvalue problems (QEPs), namely Scalable Library for Eigenvalue Problem Computations (SLEPc) and Parallel Jacobi-Davidson Package (PJDPack) are both in common built-on-top of Portable, Extensible, Toolkits for Scientific computation (PETSc) [3]. The major differeces between these two eigenpackages is that SLEPc adopts the linearization approach and provides several linear eigensolvers to solve the resulting companion GEPs. On the other hand, the PJD algorithm is the only kernel solver of PJDPack that targets directly the QEP. To draw the concrete conclusions, we generate a large number of test cases using a Matlab-based toolbox, a collection of nonlinear eigenvalue problem (NLEVP) with a diversity of matrix properties and conduct intense numerical experiments to evaluate the performance in terms of robustness, accuracy and efficiency.
