Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
PLoS One. 2022 May 9;17(5):e0267954. doi: 10.1371/journal.pone.0267954. eCollection 2022.
We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix which is based on solving a sequence of Quadratic Binary Optimization problems. This algorithm is robust across many different classes of symmetric matrices; It can compute the eigenvector/eigenvalue pair to essentially any arbitrary precision, and with minor modifications, can also solve the generalized eigenvalue problem. Performance is analyzed on small random matrices and selected larger matrices from practical applications.
我们描述了一种基于求解一系列二次二进制优化问题来计算对称矩阵的极值特征值和对应特征向量的算法。该算法在许多不同类型的对称矩阵中都具有鲁棒性;它可以计算到几乎任意精度的特征向量/特征值对,并且稍加修改,也可以解决广义特征值问题。在小随机矩阵和实际应用中选择的较大矩阵上分析了性能。