LCG：线性共轭梯度
“线性共轭梯度”（Linear Conjugate Gradient，常缩写为LCG）是一种广泛应用于数学、物理学和工程学等学术及科学领域的数值优化方法。该术语的缩写形式LCG便于文献及日常交流中的高效书写和使用，尤其在线性代数、数值分析和计算科学的相关研究中频繁出现。
Linear Conjugate Gradient具体释义
Linear Conjugate Gradient的英文发音
例句
- A hybrid algorithm is proposed according to the character of FNN structure, using the recursive least squared method to optimize the linear parameters and conjugate gradient algorithm to tune the nonlinear parameters.
- 在对模糊神经网络的参数学习算法总结归纳和性能分析的基础上，依据所研究的变结构模糊神经网络的结构特点，提出了参数混合学习算法。
- An efficient method for a large sparse linear system & qptimal conjugate gradient method
- 解任意大型线性方程组的一种有效方法&优化的共轭斜量法
- Based on the equivalence between the objective function optimization and linear equations, the proposed scheme translates the optimization model into linear equations and then performs adaptive filtering by employing an effective iterative method for linear equations, namely Conjugate Gradient ( CG ) algorithm.
- 该方案把最优化模型转化为线性方程组的形式，并用一种有效的迭代方法即共轭梯度（ConjugateGradient，CG）算法来实现自适应滤波。
- Processing a sequence of right hand sides in solving system of linear equations by the conjugate gradient method and Lanczos algorithm
- 共轭斜量法和Lanczos算法解线性方程组时一系列右端项的处理
- The linear equations are solved by using the conjugate gradient method As a result, therefore, the calculation is simplified enormously, the precision is improved, and a satisfactory result is obtained.
- 并用共轭斜量法求解，从而极大地简化了计算，提高了精度，取得了满意的结果。
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