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g, usually a constraint function
g: the gradient of f
Γ(z), for real numbers.Gamma.
Gamma with an overriding link function.
Gamma.
Gamma with an overriding link function.
Γ(z)k is an integer, is the distribution of
the sum of k independent exponentially distributed random variables,
each of which has a mean of θ (which is equivalent to a rate parameter of θ−1).γ(s, x).P(s, x).Q(s, x).Γ(s, x).Gaussian.
Gaussian with an overriding link function.
Gaussian.
Gaussian with an overriding link function.
GaussianElimination but applies only to square matrices.
f(x) = [f1(x) f2(x) ... fm(x)]'
The objective function is
F(x) = f' %*% f
m.
m.
Field.nRows rows and nCols columns,
initialized with value init.
index-th Fibonacci number, counting from 1.
index-th entry in the sequence.
i-th interval.
[row, col].
[row, col].
[row, col].
[row, col].
[row, col].
index.
t.
index.
t.
index.
col-th column, from beginRow row to endRow row, inclusively.
x.
solve operation.
row-th row, from beginCol column to endCol column, inclusively.
rowValue.
rowValue.
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- GivensMatrix(GivensMatrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
- Copy constructor performing a deep copy.
- gk -
Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.LineSearch
- the gradient evaluated at
xk
- Glejser - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
- The Glejser test is used to test for heteroskedasticity in a linear regression model.
- Glejser(Residuals) -
Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.Glejser
- Perform the Glejser test to test for heteroskedasticity in a linear regression model.
- GlmProblem - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
- This class represents a Generalized Linear regression problem.
- GlmProblem(Vector, Matrix, boolean, Family) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GlmProblem
- Construct a GLM problem.
- GlmProblem(LmProblem, Family) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.GlmProblem
- Construct a GLM problem from a linear regression problem.
- GloubKahanSVD - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.svd
- This class implements the SVD decomposition of a tall matrix using the Gloub-Kahan SVD algorithm.
- GloubKahanSVD(Matrix, boolean, boolean, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
- Perform the Gloub-Kahan SVD decomposition.
- GMT -
Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
- GMT
- Golden - Class in com.numericalmethod.suanshu.optimization.univariate
- Minimum finding algorithm by the golden section.
- Golden() -
Constructor for class com.numericalmethod.suanshu.optimization.univariate.Golden
-
- GOLDEN_RATIO -
Static variable in class com.numericalmethod.suanshu.Constant
- the Golden ratio
- GoldfeldQuandtTrotter - Class in com.numericalmethod.suanshu.optimization.unconstrained.hessian
- Goldfeld, Quandt and Trotter propose the following way
to coerce a non-positive definite Hessian matrix to become positive definite.
- GoldfeldQuandtTrotter(Matrix, double) -
Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.hessian.GoldfeldQuandtTrotter
- Construct a positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.
- Gradient - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
- 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.
- Gradient(RealScalarFunction, double...) -
Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Gradient
- Construct a gradient row matrix for a multivariate function
f at point x.
- gradient -
Variable in class com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem
- the gradient of the absolute value of the minmax objective function, for a given
ω
- GradientFunction - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
- Compute the gradient function,
g(x), for a real scalar function f(x). - GradientFunction(RealScalarFunction) -
Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
- Construct a GradientFunction to compute the gradient numerically.
- GramSchmidt - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.qr
- The Gram–Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.
- GramSchmidt(Matrix, boolean, double) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
- Construct an instance of the Gram-Schmidt process to orthogonalize a matrix.
- GramSchmidt(Matrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
- Construct an instance of the Gram-Schmidt process to orthogonalize a matrix.
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SuanShu, a Java numerical and statistical library | |||||||
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