Description: 拉格朗日插值多项式拟合,牛顿插值多项式,欧拉方程解偏微分方程,使用极限微分求解导数（微分）,微分方程组的N=4龙格库塔解法,雅可比爹迭代法解方程AX=B,最小二乘多项式拟合,组合辛普生公式求解积分,用三角分解法解方程AX=B-Lagrange interpolation polynomial fitting, polynomial interpolation Newton, Euler equations partial differential equations, Limit the use of derivative solving differential (differential), the equations of N = 4 Runge - Kutta method. Jacobian Davis iterative method of solving equations AX = B, least squares polynomial fitting, portfolio Simpson formula for integration, with a triangular decomposition method of solving equations AX = B. Platform: |
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Author:wangshen |
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Description: 拉格朗日插值多项式拟合,牛顿插值多项式,欧拉方程解偏微分方程,使用极限微分求解导数（微分）,微分方程组的N=4龙格库塔解法,雅可比爹迭代法解方程AX=B,最小二乘多项式拟合,组合辛普生公式求解积分,用三角分解法解方程AX=B-Lagrange interpolation polynomial fitting, polynomial interpolation Newton, Euler equations partial differential equations, Limit the use of derivative solving differential (differential), the equations of N = 4 Runge- Kutta method. Jacobian Davis iterative method of solving equations AX = B, least squares polynomial fitting, portfolio Simpson formula for integration, with a triangular decomposition method of solving equations AX = B. Platform: |
Size: 7168 |
Author:wangshen |
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Description: Semi-automatic Differentiation (SD) Toolkit is a Matlab implementation
of the complex step derivative (CSD) technique for the differentiation of real-valued functions. The Toolkit consists of three core functions:
sdGrad.m - Returns CSD approximation of the gradient (g) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdJac.m - Returns CSD approximation of the Jacobian (J) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdHg.m - Rerurns CSD approximation of the Hessian (H) of the
scalar-valued target function fun(p,Extra), according to Equation 7 of the
paper. It also returns the centered-difference CSD approximation of the
gradient as a by-product.
For a brief describtion of the functions in the toolkit,
type ,help sdToolkit> at Matlab command prompt.-The sdToolkit demonstrates the complex step derivative method on a variety of functions and geophysically oriented examples Platform: |
Size: 28672 |
Author:蒋礼 |
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Description: 在向量分析中, 雅可比矩阵是一阶偏导数以一定方式排列成的矩阵, 其行列式称为雅可
比行列式. 还有, 在代数几何中, 代数曲线的雅可比量表示雅可比簇：伴随该曲线的一个
代数群, 曲线可以嵌入其中.海森矩阵(Hessian matrix或Hessian)是一个自变量为向量的实值函数的二阶偏导数组成的方块矩阵.-In the vector analysis, the Jacobi matrix is a matrix of partial derivatives in a certain way are arranged, the determinant is called Jacob
Than the determinant. Also, in algebraic geometry, the Jacobian proportion of algebraic curves said the Jacobian variety: the curve of an algebraic group of adjoint curve can be embedded in them. The Hessian matrix (Hessian matrix or the Hessian) is an argument for vector valued function of the second order partial derivative consisting of a square matrix. Platform: |
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Author:lwqq |
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