论文标题
用于截短的最小二乘解决方案的量子启发的算法
Quantum-inspired algorithm for truncated total least squares solution
论文作者
论文摘要
总的最小二乘方法（TLS）方法已被广泛用于数据拟合中。与最小二乘法相比，对于TLS问题，我们不仅要考虑到观察误差，还考虑了测量矩阵中的误差。这在实际应用中更现实。对于大规模离散的问题$ ax \近B $，我们引入了量子启发的技术，以近似截断的总最小二乘（TTLS）解决方案。我们分析了量子启发的截短总正方形算法的准确性，并执行数值实验以证明我们方法的效率。
Total least squares (TLS) methods have been widely used in data fitting. Compared with the least squares method, for TLS problem we takes into account not only the observation errors, but also the errors in the measurement matrix. This is more realistic in practical applications. For the large-scale discrete ill-posed problem $Ax \approx b$, we introduce the quantum-inspired techniques to approximate the truncated total least squares (TTLS) solution. We analyze the accuracy of the quantum-inspired truncated total least squares algorithm and perform numerical experiments to demonstrate the efficiency of our method.
