预处理

Preconditioning is crucial for solving linear systems in numerical analysis.
在数值分析中，预处理对于解决线性系统至关重要。
The iterative solver benefits from preconditioning, as it accelerates convergence rates.
迭代求解器得益于预处理，因为它能加快收敛速度。
In machine learning, preconditioning techniques can improve the efficiency of gradient descent algorithms.
在机器学习中，预处理技术可以提高梯度下降算法的效率。
Preconditioning is often applied to sparse matrices to reduce computational complexity.
预处理常常用于稀疏矩阵，以减少计算复杂性。
Domain decomposition methods frequently incorporate preconditioners for better parallel performance.
域分解方法通常包含预处理器，以提高并行性能。
Linear systems with ill-conditioned matrices require specialized preconditioners for accurate solutions.
条件不良的线性系统需要专门的预处理技术以获得准确解。
In image processing, preconditioning techniques can enhance image quality before applying filters.
在图像处理中，预处理技术可以在应用滤波器前改善图像质量。
Preconditioning helps to alleviate the effects of large condition numbers in matrix operations.
预处理有助于减轻大条件数对矩阵运算的影响。
When solving eigenvalue problems, preconditioning can make the computation more tractable.
解决特征值问题时，预处理可以使计算变得更容易处理。
Adaptive preconditioning adjusts to the problem's characteristics, providing optimal performance.
自适应预处理会根据问题特性进行调整，提供最优性能。