Uses of Interface
dev.nm.analysis.function.Function

Packages that use Function

Package	Description
dev.nm.analysis.curvefit.interpolation
dev.nm.analysis.differentiation
dev.nm.analysis.differentiation.multivariate
dev.nm.analysis.differentiation.univariate
dev.nm.analysis.function
dev.nm.analysis.function.matrix
dev.nm.analysis.function.polynomial
dev.nm.analysis.function.rn2r1
dev.nm.analysis.function.rn2r1.univariate
dev.nm.analysis.function.rn2rm
dev.nm.analysis.function.special
dev.nm.analysis.function.special.beta
dev.nm.analysis.function.special.gamma
dev.nm.analysis.function.special.gaussian
dev.nm.misc.algorithm
dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
dev.nm.solver.multivariate.constrained.general.penaltymethod
dev.nm.solver.multivariate.unconstrained
dev.nm.stat.evt.evd.univariate.fitting.acer
dev.nm.stat.evt.function
dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
dev.nm.stat.stochasticprocess.univariate.filtration
dev.nm.stat.timeseries.linear.multivariate
dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
dev.nm.stat.timeseries.linear.univariate
dev.nm.stat.timeseries.linear.univariate.sample
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
tech.nmfin.portfoliooptimization.lai2010.ceta
tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
tech.nmfin.portfoliooptimization.socp.constraints
tech.nmfin.portfoliooptimization.socp.constraints.ybar

Uses of Function in dev.nm.analysis.curvefit.interpolation

Classes in dev.nm.analysis.curvefit.interpolation that implement Function

Modifier and Type	Class	Description
class	LinearInterpolator	Define a univariate function by linearly interpolating between adjacent points.
class	NevilleTable	Neville's algorithm is a polynomial interpolation algorithm.

Uses of Function in dev.nm.analysis.differentiation

Classes in dev.nm.analysis.differentiation that implement Function

Modifier and Type	Class	Description
class	Ridders	Ridders' method computes the numerical derivative of a function.

Uses of Function in dev.nm.analysis.differentiation.multivariate

Classes in dev.nm.analysis.differentiation.multivariate that implement Function

Modifier and Type	Class	Description
class	GradientFunction	The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.
class	HessianFunction	The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.
class	JacobianFunction	The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.
class	MultivariateFiniteDifference	A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.

Uses of Function in dev.nm.analysis.differentiation.univariate

Classes in dev.nm.analysis.differentiation.univariate that implement Function

Modifier and Type	Class	Description
class	DBeta	This is the first order derivative function of the Beta function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\).
class	DBetaRegularized	This is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w.r.t the upper limit, x.
class	DErf	This is the first order derivative function of the Error function, Erf.
class	Dfdx	The first derivative is a measure of how a function changes as its input changes.
class	DGamma	This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).
class	DGaussian	This is the first order derivative function of a Gaussian function, \({d \mathrm{\phi}(x) \over dx}\).
class	DPolynomial	This is the first order derivative function of a Polynomial, which, again, is a polynomial.
class	FiniteDifference	A finite difference (divided by a small increment) is an approximation of the derivative of a function.

Uses of Function in dev.nm.analysis.function

Classes in dev.nm.analysis.function that implement Function

Modifier and Type	Class	Description
class	SubFunction<R>	A sub-function, g, is defined over a subset of the domain of another (original) function, f.

Fields in dev.nm.analysis.function declared as Function

Modifier and Type	Field	Description
protected final Function<Vector,R>	SubFunction.f	the original, unrestricted function

Constructors in dev.nm.analysis.function with parameters of type Function

Modifier	Constructor	Description
	SubFunction(Function<Vector,R> f, Map<Integer,Double> fixing)	Constructs a sub-function.

Uses of Function in dev.nm.analysis.function.matrix

Subinterfaces of Function in dev.nm.analysis.function.matrix

Modifier and Type	Interface	Description
interface	RntoMatrix	This interface is a function that maps from Rn to a Matrix space.

Classes in dev.nm.analysis.function.matrix that implement Function

Modifier and Type	Class	Description
class	R1toConstantMatrix	A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).
class	R1toMatrix	This is a function that maps from R1 to a Matrix space.
class	R2toMatrix	This is a function that maps from R2 to a Matrix space.

Uses of Function in dev.nm.analysis.function.polynomial

Classes in dev.nm.analysis.function.polynomial that implement Function

Modifier and Type	Class	Description
class	CauchyPolynomial	The Cauchy's polynomial of a polynomial takes this form:
class	Polynomial	A polynomial is a UnivariateRealFunction that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.
class	QuadraticMonomial	A quadratic monomial has this form: x2 + ux + v.
class	ScaledPolynomial	This constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow.

Uses of Function in dev.nm.analysis.function.rn2r1

Subinterfaces of Function in dev.nm.analysis.function.rn2r1

Modifier and Type	Interface	Description
interface	BivariateRealFunction	A bivariate real function takes two real arguments and outputs one real value.
interface	RealScalarFunction	A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).
interface	TrivariateRealFunction	A trivariate real function takes three real arguments and outputs one real value.

Classes in dev.nm.analysis.function.rn2r1 that implement Function

Modifier and Type	Class	Description
class	AbstractBivariateRealFunction	A bivariate real function takes two real arguments and outputs one real value.
class	AbstractRealScalarFunction	This abstract implementation implements dimensionOfRange() by always returning 1, and dimensionOfDomain() by returning the input argument for the dimension of domain.
class	AbstractTrivariateRealFunction	A trivariate real function takes three real arguments and outputs one real value.
class	QuadraticFunction	A quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
class	R1Projection	Projection creates a real-valued function RealScalarFunction from a vector-valued function RealVectorFunction by taking only one of its coordinate components in the vector output.
class	RealScalarSubFunction	This constructs a RealScalarFunction from another RealScalarFunction by restricting/fixing the values of a subset of variables.

Uses of Function in dev.nm.analysis.function.rn2r1.univariate

Subinterfaces of Function in dev.nm.analysis.function.rn2r1.univariate

Modifier and Type	Interface	Description
interface	UnivariateRealFunction	A univariate real function takes one real argument and outputs one real value.

Classes in dev.nm.analysis.function.rn2r1.univariate that implement Function

Modifier and Type	Class	Description
class	AbstractUnivariateRealFunction	A univariate real function takes one real argument and outputs one real value.
class	ContinuedFraction	A continued fraction representation of a number has this form: \[ z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}} \] ai and bi can be functions of x, which in turn makes z a function of x.
class	StepFunction	A step function (or staircase function) is a finite linear combination of indicator functions of intervals.

Uses of Function in dev.nm.analysis.function.rn2rm

Subinterfaces of Function in dev.nm.analysis.function.rn2rm

Modifier and Type	Interface	Description
interface	RealVectorFunction	A vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).

Classes in dev.nm.analysis.function.rn2rm that implement Function

Modifier and Type	Class	Description
class	AbstractR1RnFunction	This is a function that takes one real argument and outputs one vector value.
class	AbstractRealVectorFunction	This abstract implementation implements dimensionOfDomain() and dimensionOfRange() by returning the input arguments at constructor.
class	RealVectorSubFunction	This constructs a RealVectorFunction from another RealVectorFunction by restricting/fixing the values of a subset of variables.

Uses of Function in dev.nm.analysis.function.special

Classes in dev.nm.analysis.function.special that implement Function

Modifier and Type	Class	Description
class	Rastrigin	The Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.

Uses of Function in dev.nm.analysis.function.special.beta

Classes in dev.nm.analysis.function.special.beta that implement Function

Modifier and Type	Class	Description
class	Beta	The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]
class	BetaRegularized	The Regularized Incomplete Beta function is defined as: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]
class	BetaRegularizedInverse	The inverse of the Regularized Incomplete Beta function is defined at: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]
class	LogBeta	This class represents the log of Beta function log(B(x, y)).
class	MultinomialBetaFunction	A multinomial Beta function is defined as: \[ \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left(\sum_{i=1}^K \alpha_i\right)},\qquad\boldsymbol{\alpha}=(\alpha_1,\cdots,\alpha_K) \]

Uses of Function in dev.nm.analysis.function.special.gamma

Classes in dev.nm.analysis.function.special.gamma that implement Function

Modifier and Type	Class	Description
class	Digamma	The digamma function is defined as the logarithmic derivative of the gamma function.
class	GammaGergoNemes	The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.
class	GammaLanczos	Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.
class	GammaLanczosQuick	Lanczos approximation, computations are done in double.
class	GammaLowerIncomplete	The Lower Incomplete Gamma function is defined as: \[ \gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s) \] P(s,x) is the Regularized Incomplete Gamma P function.
class	GammaRegularizedP	The Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]
class	GammaRegularizedPInverse	The inverse of the Regularized Incomplete Gamma P function is defined as: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] When s > 1, we use the asymptotic inversion method. When s <= 1, we use an approximation of P(s,x) together with a higher-order Newton like method. In both cases, the estimated value is then improved using Halley's method, c.f., HalleyRoot.
class	GammaRegularizedQ	The Regularized Incomplete Gamma Q function is defined as: \[ Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0 \] The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.
class	GammaUpperIncomplete	The Upper Incomplete Gamma function is defined as: \[ \Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s) \] The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable.
class	LogGamma	The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.
class	Trigamma	The trigamma function is defined as the logarithmic derivative of the digamma function.

Uses of Function in dev.nm.analysis.function.special.gaussian

Classes in dev.nm.analysis.function.special.gaussian that implement Function

Modifier and Type	Class	Description
class	CumulativeNormalHastings	Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
class	CumulativeNormalInverse	The inverse of the cumulative standard Normal distribution function is defined as: \[ N^{-1}(u) /]
class	CumulativeNormalMarsaglia	Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.
class	Erf	The Error function is defined as: \[ \operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt \]
class	Erfc	This complementary Error function is defined as: \[ \operatorname{erfc}(x) = 1-\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt \]
class	ErfInverse	The inverse of the Error function is defined as: \[ \operatorname{erf}^{-1}(x) \]
class	Gaussian	The Gaussian function is defined as: \[ f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } } \]

Uses of Function in dev.nm.misc.algorithm

Methods in dev.nm.misc.algorithm with parameters of type Function

Modifier and Type	Method	Description
double[]	Bins.getBinObjectValues(Function<List<T>,Double> f)	Applies a function to the items of each bin.

Constructors in dev.nm.misc.algorithm with parameters of type Function

Modifier	Constructor	Description
	BruteForce(Function<D,R> function)	Constructs a brute force search for a function.

Uses of Function in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Classes in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization that implement Function

Modifier and Type	Class	Description
class	MarketImpact1	Constructs the constraint coefficient arrays of a market impact term in the compact form.
class	PortfolioRiskExactSigma	Constructs the constraint coefficient arrays of the portfolio risk term in the compact form.
class	SOCPPortfolioConstraint	An SOCP constraint for portfolio optimization, e.g., market impact, is represented by a set of constraints in this form: \[ ||A^{T}x+c||_{2}\leq b^{T}x+d \] or this form: /[ A^T x = c, x \in \Re^m /] or this form: /[ A^T x \leq c, x \in \Re^m /]
class	SOCPPortfolioObjectiveFunction	Constructs the objective function for portfolio optimization.
class	SOCPRiskConstraint

Uses of Function in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem

Classes in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem that implement Function

Modifier and Type	Class	Description
class	QPProblemOnlyEqualityConstraints	A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.

Uses of Function in dev.nm.solver.multivariate.constrained.general.penaltymethod

Classes in dev.nm.solver.multivariate.constrained.general.penaltymethod that implement Function

Modifier and Type	Class	Description
class	AbsoluteErrorPenalty	This penalty function sums up the absolute error penalties.
class	CourantPenalty	This penalty function sums up the squared error penalties.
class	FletcherPenalty	This penalty function sums up the squared costs penalties.
class	MultiplierPenalty	A multiplier penalty function allows different weights to be assigned to the constraints.
class	PenaltyFunction	A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.
class	SumOfPenalties	This penalty function sums up the costs from a set of constituent penalty functions.
class	ZeroPenalty	This is a dummy zero cost (no cost) penalty function.

Uses of Function in dev.nm.solver.multivariate.unconstrained

Methods in dev.nm.solver.multivariate.unconstrained with parameters of type Function

Modifier and Type	Method	Description
BruteForceMinimizer<R>.Solution	BruteForceMinimizer.solve(Function<Vector,R> f)

Uses of Function in dev.nm.stat.evt.evd.univariate.fitting.acer

Classes in dev.nm.stat.evt.evd.univariate.fitting.acer that implement Function

Modifier and Type	Class	Description
class	ACERFunction	The ACER (Average Conditional Exceedance Rate) function \(\epsilon_k(\eta)\) approximates the probability \[ \epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta) \] for a sequence of stochastic process observations \(X_i\) with a k-step memory.
class	ACERInverseFunction	The inverse of the ACER function.
class	ACERLogFunction	The ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).
class	ACERReturnLevel	Given an ACER function, compute the return level \(\eta\) for a given return period \(R\).

Uses of Function in dev.nm.stat.evt.function

Classes in dev.nm.stat.evt.function that implement Function

Modifier and Type	Class	Description
class	ReturnLevel	Given a GEV distribution of a random variable \(X\), the return level \(\eta\) is the value that is expected to be exceeded on average once every interval of time \(T\), with a probability of \(1 / T\).
class	ReturnPeriod	The return period \(R\) of a level \(\eta\) for a random variable \(X\) is the mean number of trials that must be done for \(X\) to exceed \(\eta\).

Uses of Function in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction

Classes in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction that implement Function

Modifier and Type	Class	Description
class	GaussianProposalFunction	A proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix.
class	HybridMCMCProposalFunction
class	ProposalFunction	A proposal function goes from the current state to the next state, where a state is a vector.

Uses of Function in dev.nm.stat.stochasticprocess.univariate.filtration

Classes in dev.nm.stat.stochasticprocess.univariate.filtration that implement Function

Modifier and Type	Class	Description
class	Bt	This is a FiltrationFunction that returns \(B(t_i)\), the Brownian motion value at the i-th time point.
class	F_Sum_BtDt	This represents a function of this integral \[ I = \int_{0}^{1} B(t)dt \]
class	F_Sum_tBtDt	This represents a function of this integral \[ \int_{0}^{1} (t - 0.5) * B(t) dt \]
class	FiltrationFunction	A filtration function, parameterized by a fixed filtration, is a function of time, \(f(\mathfrak{F_{t_i}})\).

Uses of Function in dev.nm.stat.timeseries.linear.multivariate

Classes in dev.nm.stat.timeseries.linear.multivariate that implement Function

Modifier and Type	Class	Description
class	MultivariateAutoCorrelationFunction	This is the auto-correlation function of a multi-dimensional time series {Xt}.
class	MultivariateAutoCovarianceFunction	This is the auto-covariance function of a multi-dimensional time series {Xt}, \[ K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)') \] For a stationary process, the auto-covariance depends only on the lag, |i - j|.

Uses of Function in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Classes in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma that implement Function

Modifier and Type	Class	Description
class	VARMAAutoCorrelation	Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
class	VARMAAutoCovariance	Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.

Uses of Function in dev.nm.stat.timeseries.linear.univariate

Classes in dev.nm.stat.timeseries.linear.univariate that implement Function

Modifier and Type	Class	Description
class	AutoCorrelationFunction	This is the auto-correlation function of a univariate time series {xt}.
class	AutoCovarianceFunction	This is the auto-covariance function of a univariate time series {xt}.

Uses of Function in dev.nm.stat.timeseries.linear.univariate.sample

Classes in dev.nm.stat.timeseries.linear.univariate.sample that implement Function

Modifier and Type	Class	Description
class	SampleAutoCorrelation	This is the sample Auto-Correlation Function (ACF) for a univariate data set.
class	SampleAutoCovariance	This is the sample Auto-Covariance Function (ACVF) for a univariate data set.
class	SamplePartialAutoCorrelation	This is the sample partial Auto-Correlation Function (PACF) for a univariate data set.

Uses of Function in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that implement Function

Modifier and Type	Class	Description
class	AutoCorrelation	Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
class	AutoCovariance	Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.

Uses of Function in tech.nmfin.portfoliooptimization.lai2010.ceta

Classes in tech.nmfin.portfoliooptimization.lai2010.ceta that implement Function

Modifier and Type	Class	Description
class	Ceta	The function C(η) to be maximized (Eq.

Uses of Function in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer

Classes in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer that implement Function

Modifier and Type	Class	Description
static class	CetaMaximizer.NegCetaFunction

Uses of Function in tech.nmfin.portfoliooptimization.socp.constraints

Classes in tech.nmfin.portfoliooptimization.socp.constraints that implement Function

Modifier and Type	Class	Description
class	SOCPLinearBlackList	A black list means that the positions of some assets must be zero.
class	SOCPLinearMaximumLoan	A maximum loan constraint.
class	SOCPLinearSectorNeutrality	A sector neutrality means that the sum of weights for given sectors are zero.
class	SOCPLinearSelfFinancing	A self financing constraint.
class	SOCPLinearZeroValue	A zero value constraint.
class	SOCPMaximumLoan	Transforms a maximum loan constraint into the compact SOCP form.
class	SOCPNoTradingList1	Transforms a black list (not to trade a new position) constraint into the compact SOCP form.
class	SOCPSectorNeutrality	Transforms a sector neutral constraint into the compact SOCP form.
class	SOCPSelfFinancing	Transforms a self financing constraint into the compact SOCP form.
class	SOCPZeroValue	Transforms a zero value constraint into the compact SOCP form.

Uses of Function in tech.nmfin.portfoliooptimization.socp.constraints.ybar

Classes in tech.nmfin.portfoliooptimization.socp.constraints.ybar that implement Function

Modifier and Type	Class	Description
class	SOCPLinearSectorExposure	A sector exposure constraint.
class	SOCPNoTradingList2	Transforms a black list (not to trade a new position) constraint into the compact SOCP form.
class	SOCPSectorExposure	Transforms a sector exposure constraint into the compact SOCP form.