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#Com.numericalmethod.suanshu.analysis.differentiation Classes and Interfaces - 21 results found.
| Name | Description | Type | Package | Framework |
| BorderedHessian | A bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| C1 | See Also:Wikipedia: Smooth functionGet the gradient function, g, of a real valued function f. | Interface | com.numericalmethod.suanshu.analysis.differentiation.differentiability | SuanShu |
| C2 | Interface | com.numericalmethod.suanshu.analysis.differentiation.differentiability | SuanShu | |
| DBeta | This is the first order derivative function of the Beta function w. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| DBetaRegularized | This is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| DErf | This is the first order derivative function of the Error function, Erf. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| Dfdx | The first derivative is a measure of how a function changes as its input changes. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| Dfdx .Method | the available methods to compute the numerical derivativeFinite difference: approximate a derivative using grid points. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| DGamma | This is the first order derivative function of the Gamma function, ({d mathrm{Gamma}(x) over dx}). | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| DGaussian | This is the first order derivative function of a Gaussian function, ({d mathrm{phi}(x) over dx}). | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| DPolynomial | This is the first order derivative function of a Polynomial, which, again, is a polynomial. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| FiniteDifference | A finite difference (divided by a small increment) is an approximation of the derivative of a The accuracy depends on the function to take the derivative of. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| FiniteDifference .Type | the available types of finite differenceReturns the enum constant of this type with the specified name. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
| Gradient | The gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field, and of which the magnitude is the greatest rate of change. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| GradientFunction | The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| Hessian | The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| HessianFunction | The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| Jacobian | The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| JacobianFunction | The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| MultivariateFiniteDifference | A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
| Ridders | Ridders' method computes the numerical derivative of a function. | Class | com.numericalmethod.suanshu.analysis.differentiation | SuanShu |