3DDC

Matlab曲面拟合程序,利用最小二乘法来求得近似值，非常不错的东西(matlab program for fit curve)

矩量法的fortran程序，利用矩量法求解球散射的程序，希望大家好好学习(mom fortran program)

　禁忌搜索算法是一种全局性邻域搜索算法，模拟人类具有记忆功能的寻优特征。它通过局部邻域搜索机制和相应的禁忌准则来避免迂回搜索，并通过破禁水平来释放一些被禁忌的优良状态，进而保证多样化的有效探索，以最终实现全局优化(Tabu search algorithm)

CT迭代重建算法中3维投影矩阵的生成算法，该算法对学习计算机断层层析成像很有帮助。(CT image reconstruction projection ,3D)

详细全面的蒙特卡洛PPT讲解以及MATLAB算例，帮助大家0基础快速掌握MC的精华所在。(Detailed and comprehensive Monte Carlo PPT to explain and MATLAB example, to help you basically grasp the essence of MC.)

利用最小二乘方法，对三维数据进行平面拟合，并对数据进行误差提出(least square plane fitting )

说明：  STATCOM STATIC COMPENSATOR

从旋转矩阵到四元数的转换，matlab代码。(From the rotation matrix to quaternion conversion, matlab code.)

可控源电磁法（CSEM）一维反演程序，利用Occam算法(1d inversion program for CSEM)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)